/normxcorr/trunk

%% written by Chris

% updated last 9/8/2010

% this function allows to track multiple peaks along one axis. The

% resolution can be much higher than the cross correlation methods of

% digital image correlation.

% function goes here

function [validx,validy]=multipeak_tracking;

%% Load image and ask for orientation and pos/neg

if exist('filenamelist')==0

    load('filenamelist')            % file with the list of filenames to be processed

end

filenumber=size(filenamelist);

filenumber=filenumber(1);

h=figure;

imshow(filenamelist(1,:))           % show the first image

title('First Image')        % put a title

ImageA=(uint8(mean(double(imread(filenamelist(1,:))),3)));

sel_pos_neg = menu(sprintf('Do you want use the negative image for analysis?'),...

    'As is', 'Negativ', 'Cancel')

if sel_pos_neg==3

    close all;

    return

end

if sel_pos_neg==2

    ImageA=255-ImageA;

    imshow(ImageA)           % show the first image

    negativ=1;

end

if sel_pos_neg==1

    negativ=0;

end

sel_horz_vert = menu(sprintf('Do you want to measure horizontal or vertical?'),...

    'Horizontal', 'Vertical', 'Cancel')

if sel_horz_vert==3

    close all;

    return

end

if sel_horz_vert==2

    ImageA=ImageA';

    imshow(ImageA)           % show the first image

    orientation=90;

end

if sel_horz_vert==1

    orientation=0;

end

[imr,imc]=size(ImageA);

%% select path for displacement extraction along markers

%Select point in middle of marker

xlabel('Location On Image [Pixels]')

ylabel('Location On Image [Pixels]')

title(['Click on the center of the sample. Width: ', num2str(imc),...

    '; Height: ',num2str(imr)])

marker_pt=round(ginput(1));

x_mark = marker_pt(1);

y_mark = marker_pt(2);

hold on

line([1 imc],[y_mark y_mark],'Color','r')

%Prompt for integration width

prompt = {'Enter integration width [Pixels]:'};

dlg_title = 'Input integration width for the analysis';

num_lines= 1;

def     = {'20'};

answer = inputdlg(prompt,dlg_title,num_lines,def);

int_width = str2num(cell2mat(answer(1)));

line([1 imc],[y_mark-int_width/2 y_mark-int_width/2],'Color','g')

line([1 imc],[y_mark+int_width/2 y_mark+int_width/2],'Color','g')

%% Calculate line profile data and select peaks

xdata = [1:1:imc];

ydata= sum(ImageA((y_mark-int_width/2):(y_mark+int_width/2),:),1)/int_width;

g=figure;

plot(xdata,ydata)

xlabel('Location On Image [Pixels]')

ylabel('Pixel Intensity Value')

sel_peakselect = menu(sprintf('Do you want to select single or all peaks?')...

    ,'Single Select', 'All Select', 'Cancel')

if sel_peakselect==3

    close all;

    return

end

% If single peaks should be chosen

if sel_peakselect==1

    sel_peakselect_2=1

    i=1

    hold on

    while sel_peakselect_2==1

        title('Click on the left of the chosen peak.')

        marker_pt=round(ginput(1));

        x_peak_left = marker_pt(1);

        y_peak_left = marker_pt(2);

        line([x_peak_left x_peak_left], [1 max(ydata)],'Color','r')

        title('Click on the right of the chosen peak.')

        marker_pt=round(ginput(1));

        x_peak_right = marker_pt(1);

        y_peak_right = marker_pt(2);

        line([x_peak_right x_peak_right], [1 max(ydata)],'Color','r')

        plot(xdata(x_peak_left:x_peak_right),ydata(x_peak_left:x_peak_right),'k')

        peak_pos(i,:)=[x_peak_left x_peak_right];

        i=i+1;

        sel_peakselect_2 = menu(sprintf('Do you want to select another peak?')...

            ,'One more', 'No, lets process')

    end

end

% If all peaks should be found automatically

if sel_peakselect==2

end

%% Start first fitting

fitcountertemp=size(peak_pos); % number off peaks to cycle through

fitcounter=fitcountertemp(1,1); % number off peaks to cycle through

for c=1:fitcounter %start the loop to process all points

    fitdatax=xdata(peak_pos(c,1):peak_pos(c,2));

    fitdatay=ydata(peak_pos(c,1):peak_pos(c,2));

    % fitting in x-direction

    % guess some parameters for the fitting routine --> bad guesses lead to

    % an error message which stops the fitting

    back_guess=(ydata(peak_pos(c,2))+ydata(peak_pos(c,2)))/2; % guess for the background level - average of the first and last greyvalue

    sig1_guess=(peak_pos(c,2)-peak_pos(c,1))/2; % guess for the peak width - take half of the cropping width

    amp_guess1=max(fitdatay); % guess for the amplitude - take the greyvalue at the peak position

    mu1_guess=(peak_pos(c,2)+peak_pos(c,1))/2; % guess for the position of the peak - take the position from bwlabel

    % start fitting routine

    [x,resnormx,residual,exitflagx,output]  = lsqcurvefit(@gauss_onepk, [amp_guess1 mu1_guess sig1_guess back_guess], fitdatax, fitdatay); %give the initial guesses and data to the gauss fitting routine

    % show the fitting results

    xtest = fitdatax; % x values for the plot of the fitting result

    ytest = (x(1)*exp((-(xtest-x(2)).^2)./(2.*x(3).^2))) + x(4); % y values of the fitting result

    yguess=(amp_guess1*exp((-(xtest-mu1_guess).^2)./(2.*sig1_guess.^2))) + back_guess; %y values for the guess plot

    plot(xtest,ytest,'r') % plot the fitted function

    plot(xtest,yguess,'b') % plot the guessed function

    drawnow

    peaks(c,1,:)=x;

end

sel_go = menu(sprintf('Do you want proceed?')...

    ,'Do it!', 'No, stop!')

if sel_peakselect==2

    close all;

    return

end

cutout=peak_pos(:,2)-peak_pos(:,1)

for m=1:(filenumber-1) % loop through all images

    ImageA=((mean(double(imread(filenamelist(m,:))),3)));

    if sel_horz_vert==2

        ImageA=ImageA';

        imshow(ImageA)           % show the first image

        orientation=90;

    end

    if sel_pos_neg==2

        ImageA=255-ImageA;

        imshow(ImageA)           % show the first image

        negativ=1;

    end

    image=m

    xdata = [1:1:imc];

    ydata= sum(ImageA((y_mark-int_width/2):(y_mark+int_width/2),:),1)/int_width;

    plot(xdata,ydata)

    title(['Image Number: ', num2str(m)])

    hold on

    for c=1:fitcounter %start the loop to process all points

        if (peaks(c,m,2)+cutout(c)/2)>imc

        fitdatax=xdata((imc-cutout(c)):imc);

        fitdatay=ydata((imc-cutout(c)):imc);

        else

        fitdatax=xdata((peaks(c,m,2)-cutout(c)/2):(peaks(c,m,2)+cutout(c)/2));

        fitdatay=ydata((peaks(c,m,2)-cutout(c)/2):(peaks(c,m,2)+cutout(c)/2));

        end

        % fitting in x-direction

        % guess some parameters for the fitting routine --> bad guesses lead to

        % an error message which stops the fitting

        back_guess=peaks(c,m,4); % guess for the background level - average of the first and last greyvalue

        sig1_guess=peaks(c,m,3); % guess for the peak width - take half of the cropping width

        amp_guess1=peaks(c,m,1); % guess for the amplitude - take the greyvalue at the peak position

        mu1_guess=peaks(c,m,2); % guess for the position of the peak - take the position from bwlabel

        % start fitting routine

        [x,resnormx,residual,exitflagx,output]  = lsqcurvefit(@gauss_onepk, [amp_guess1 mu1_guess sig1_guess back_guess], fitdatax, fitdatay); %give the initial guesses and data to the gauss fitting routine

        % show the fitting results

        xtest = fitdatax; % x values for the plot of the fitting result

        ytest = (x(1)*exp((-(xtest-x(2)).^2)./(2.*x(3).^2))) + x(4); % y values of the fitting result

        yguess=(amp_guess1*exp((-(xtest-mu1_guess).^2)./(2.*sig1_guess.^2))) + back_guess; %y values for the guess plot

        plot(xtest,ytest,'r') % plot the fitted function

        plot(xtest,yguess,'g') % plot the guessed function

        drawnow

        peaks(c,m+1,:)=x;

    end

    hold off

    Amplitude=peaks(:,m,1)';

    Peakposition=peaks(:,m,2)';

    Peakwidth=peaks(:,m,3)';

    Background=peaks(:,m,4)';

    dlmwrite('backupPeakposition.txt', Peakposition' , '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 ;-)

    dlmwrite('backupAmplitude.txt', Amplitude' , '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 ;-)

    dlmwrite('backupPeakwidth.txt', Peakwidth' , '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 ;-)

    dlmwrite('backupBackground.txt', Background' , '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 ;-)

end

%% Save data

validx=peaks(:,:,2);

validy=peaks(:,:,3);

Amplitude=peaks(:,:,1)';

Peakposition=peaks(:,:,2)';

Peakwidth=peaks(:,:,3)';

Background=peaks(:,:,4)';

save peaks;

save Amplitude.dat Amplitude -ascii -tabs;

save Peakposition.dat Peakposition -ascii -tabs;

save Peakwidth.dat Peakwidth -ascii -tabs;

save Background.dat Background -ascii -tabs;

save validx_mp.dat validx -ascii -tabs;

save validy_mp.dat validy -ascii -tabs;