www.pudn.com > Digital_Image_Correlation_2010b.zip > peak_labelling.m, change:2010-11-20,size:17860b


% written by Chris 
 
function [validx,validy]=peak_labelling; 
 
% The peak labelling function is an alternative methode to the 
% automate_image function. The difference between the two functions is, 
% that the automate_image function the correlation coefficient uses to 
% track a fixed grid of markers, while the peak_labelling function is 
% searching for peaks in a base image and tryes to fit a gauss function to 
% it. Therefore the image should have a very low background and bright 
% round peaks. If this is the case peak_labelling will find these peaks fit 
% the gauss function in x and y direction to each of the peaks and then 
% tracks all peaks in all images. The output files are fitxy.dat, 
% validx.dat and validy.dat, which will end up in the Current directory of 
% matlab. Attention with each run these files will be overwritten.  
% 
% The peak_labelling function is a bit less sensible to image noise, is 
% only tracking markers in the image which are actually there and can under 
% certain circumstances provide a higher accuracy for larger markers. 
 
 
[Image,PathImage] = uigetfile('*.tif','Open Image'); 
cd(PathImage) 
load('filenamelist') 
filenumber=length(filenamelist); 
% filelist_generator 
 
I=imread(Image); % read Image 
Itemp=mean(double(I),3); 
I=Itemp; 
figure, image(I); %show Image 
axis('equal'); 
drawnow 
title(sprintf('Mark the region of interest: Click on the on the lower left corner and and then on the upper right corner')) 
[xprof, yprof]=ginput(2); % Get the Area of Interest 
xmin = xprof(1,1); 
xmax = xprof(2,1); 
ymin = yprof(2,1); 
ymax= yprof(1,1); 
tic; % start timer for time estimation 
msgboxwicon=msgbox('Subtracting image background, please wait.','Processing...') 
I2 = imsubtract (I, imopen(I,strel('disk',15))); % subtract background 
close(msgboxwicon) 
image(I2); %show with subtracted background 
axis('equal'); 
t(1,1)=toc; 
tic; 
drawnow 
roi=(I2>10); %subtract greyvalues to work only with real peaks 
[labeled,numObjects] = bwlabel(roi,8); %label all peaks - very important function, crucial for the whole process; see matlab manual 
powderdata=regionprops(labeled,'basic') % get peak properties from bwlabel 
powderarea=[powderdata.Area]; %define area variable 
powdercentroid=[powderdata.Centroid]; %define position variable 
powderboundingbox=[powderdata.BoundingBox]; %define bounding box variable 
counter=0; 
countermax=length(powdercentroid)/2; 
powderxy=zeros(countermax,8); 
for i=1:countermax; % get all data from the bwlabel (position, bounding box and area of peaks)  
    counter=counter+1; % 
    powderxy(i,1)=i; % number of the detected particle 
    powderxy(i,2)=powdercentroid(1, (i*2-1)); % x coordinate of particle position 
    powderxy(i,3)=powdercentroid(1, (i*2)); % y coordinate of particle position 
    powderxy(i,4)=powderboundingbox(1, (i*4)-3); % x coordinate of bounding box 
    powderxy(i,5)=powderboundingbox(1, (i*4)-2); % y coordinate of bounding box 
    powderxy(i,6)=powderboundingbox(1, (i*4)-1); % width (x) of bounding box 
    powderxy(i,7)=powderboundingbox(1, (i*4)); % height (y) of bounding box 
    powderxy(i,8)=powderarea(1, i); % area of bounding box 
end 
 
 
% cropping in x direction to reduce to the area of interest 
% crop away peaks which are too small or too big 
 
Amin=10; %minimum peaksize 10 pixel --> only testwise 
Amax=1000; %maximum peaksize --> only testwise 
counter=0 
i=0; 
 
% throw away useless peaks (defined by position and size) 
for i=1:countermax; % loop through all points 
     
    if xmin<powderxy(i,2) % crop all points left from Region Of Interest (ROI) 
        if powderxy(i,2)<xmax % crop all points right from Region Of Interest (ROI) 
            if ymin<powderxy(i,3) % crop all points below the Region Of Interest (ROI) 
                if powderxy(i,3)<ymax % crop all points above the Region Of Interest (ROI) 
                    if Amin<powderxy(i,8) % crop all points with a small peak area  
                        if powderxy(i,8)<Amax % crop all points with a too big area 
                            counter=counter+1; 
                            cropxy(counter,1)=counter; % peaks get a new number  
                            cropxy(counter,2)=powderxy(i,2); % x 
                            cropxy(counter,3)=powderxy(i,3); % y 
                            cropxy(counter,4)=powderxy(i,4); % x bounding box 
                            cropxy(counter,5)=powderxy(i,5); % y bounding box 
                            cropxy(counter,6)=powderxy(i,6); % width (x) bounding box 
                            cropxy(counter,7)=powderxy(i,7); % height (y) bounding box 
                            cropxy(counter,8)=powderxy(i,8); % area bounding box 
                        end 
                    end 
                end 
            end 
        end 
    end 
end 
 
% clear variables 
clear powderxy 
clear powderdata 
clear powderarea 
clear powdercentroid 
clear powderboundingbox 
clear counter 
clear countermax 
clear Amin 
clear Amax 
clear roi 
 
close all 
 
t(1,2)=toc; % stop timer 
 
 
% Start fitting process of the peaks, labeled by bwlabel 
 
tic; % start timer 
counter=0 
g = waitbar(0,'Processing image'); % nucleating the progress bar 
fitcountertemp=size(cropxy); % 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 
    waitbar(c/(fitcounter-1)); % growth of the progress bar 
    cropI=imcrop(I,[(round(cropxy(c,2))-round(cropxy(c,6))) (round(cropxy(c,3))-round(cropxy(c,7))) round(cropxy(c,6))*2 round(cropxy(c,7))*2]); % crop the region around the detected peak (bwlabel) 
     
% get line profile in x direction for the fitting routine 
     
    xdata = [(round(cropxy(c,2))-round(cropxy(c,6))):1:(round(cropxy(c,2))+round(cropxy(c,6)))]; % x-coordinate for the fitting which is equivalent to the x coordinate in the image 
    ydata=sum(cropI)/(2*cropxy(c,7)); % y-coordinate for the fitting which is equivalent to the greyvalues in the image - integrated in y direction of the image 
     
% 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(1)+ydata(round(cropxy(c,6))*2))/2; % guess for the background level - averadge of the first and last greyvalue 
    sig1_guess=(cropxy(c,6))/5; % guess for the peak width - take a fith of the cropping width 
    amp_guess1=ydata(round(cropxy(c,6))); % guess for the amplitude - take the greyvalue at the peak position 
    mu1_guess=cropxy(c,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], xdata, ydata); %give the initial guesses and data to the gauss fitting routine 
 
% show the fitting results 
    xtest = [(round(cropxy(c,2))-round(cropxy(c,6))):0.1:(round(cropxy(c,2))+round(cropxy(c,6)))]; % 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(xdata,ydata,'o') % plot the experimental data 
%     hold on 
%     plot(xtest,ytest,'r') % plot the fitted function 
%     plot(xtest,yguess,'b') % plot the guessed function     
%     drawnow 
%     hold off 
     
% fitting in y-direction 
    % guess parameters for the fitting routine --> bad guesses lead to 
    % an error message which stops the fitting 
    
% get line profile in x direction for the fitting routine 
     
    xdata = [(round(cropxy(c,3))-round(cropxy(c,7))):1:(round(cropxy(c,3))+round(cropxy(c,7)))]; % x data in y direction of the image 
    ydata=sum(cropI')/(2*cropxy(c,6)); % integrate greyvalues in x direction and normalize it to the number of integrated lines 
     
% fitting in y-direction 
    % guess parameters for the fitting routine --> bad guesses lead to 
    % an error message which stops the fitting 
    
    back_guess=(ydata(1)+ydata(round(cropxy(c,7))*2))/2; % guess for the background level - averadge of the first and last greyvalue 
    sig1_guess=(cropxy(c,6))/5; % guess for the peak width - take a fith of the cropping width 
    amp_guess1=ydata(round(cropxy(c,7))); % guess for the amplitude - take the greyvalue at the peak position 
    mu1_guess=cropxy(c,3); % guess for the position of the peak - take the position from bwlabel 
     
% start fitting routine 
    [y,resnormy,residual,exitflagy,output]  = lsqcurvefit(@gauss_onepk, [amp_guess1 mu1_guess sig1_guess back_guess], xdata, ydata); %give the initial guesses and data to the gauss fitting routine 
     
% show the fitting results 
    xtest = [(round(cropxy(c,3))-round(cropxy(c,7))):0.1:(round(cropxy(c,3))+round(cropxy(c,7)))]; % x values for the plot of the fitting result 
    ytest = (y(1)*exp((-(xtest-y(2)).^2)./(2.*y(3).^2))) + y(4); % y values of the fitting result 
    xguess = [(round(cropxy(c,3))-round(cropxy(c,7))):0.1:(round(cropxy(c,3))+round(cropxy(c,7)))]; % x values for the guess 
    yguess = (amp_guess1*exp((-(xguess-mu1_guess.^2)./(2.*sig1_guess.^2))) + back_guess); % y values for the guess plpot 
%     plot(xdata,ydata,'o') % plot the experimental data 
%     hold on 
%     plot(xtest,ytest,'g') % plot the fitted function 
%     plot(xguess,yguess,'b') % plot the guessed function     
%     drawnow 
%     hold off 
     
% sort out the bad points and save the good ones in fitxy  
    % this matrix contains the to be used points from the first image 
     
    if exitflagx>0 % if the fitting routine didn't find end before the 4000th iteration (check that in lsqcurvefit.m) then exitflag will be equal or smaller then 0 
        if exitflagy>0 % the same for the y direction fitting 
            if x(3)>1 % the width of the peak should be wider than 1 pixel - this is negotiable: different powder particle or cameras can give back results with very narrow peaks 
                if y(3)>1 % the same for y direction fitting 
                    if resnormx/cropxy(c,6)<10 % A measure of the "happyness" of a fit is the residual, the difference between the observed and predicted data. (in the help file: Mathematics: Data Analysis and Statistics: Analyzing Residuals) 
                        if resnormy/cropxy(c,7)<10 % the same for the y- direction - - - a good value is as far as I know until now between 30 and 50. The good fits stay well beyond that (between 0 and 10) 
                            counter=counter+1;  
                            fitxy(counter,1)=c; % points  get their final number  
                            fitxy(counter,2)=abs(x(1)); % fitted amplitude x-direction 
                            fitxy(counter,3)=abs(x(2)); % fitted position of the peak x-direction 
                            fitxy(counter,4)=abs(x(3)); % fitted peak width in x-direction 
                            fitxy(counter,5)=(x(4)); % fitted background in x-direction 
                            fitxy(counter,6)=abs(y(1)); % fitted amplitude y-direction 
                            fitxy(counter,7)=abs(y(2)); % fitted position of the peak y-direction 
                            fitxy(counter,8)=abs(y(3)); % fitted peak width in y-direction 
                            fitxy(counter,9)=abs(y(4)); % fitted background in y-direction 
                            fitxy(counter,10)=cropxy(c,6); % cropping width in x-direction 
                            fitxy(counter,11)=cropxy(c,7); % cropping width in ydirection 
                        end 
                    end 
                end 
            end 
        end 
    end 
     
end 
 
 
close(g) % close progress bar window 
t(1,3)=toc; % stop timer 
image_time_s=t(1,3); % take time per image 
estimated_totaltime_h=image_time_s*filenumber/3600 % calculate estimated time 
sum(t); 
total_time_h=sum(t) 
close all 
 
% plot image with peaks labeled by bwlabel (crosses) and the chosen points 
% which are easy to fit with a gaussian distribution (circles) 
 
figure, image(I2); %show Image 
title(['Number of selected Images: ', num2str(filenumber), '; Estimated time [h] ', num2str((round(estimated_totaltime_h*10)/10)), ' Crosses are determined peaks, circles are chosen for  the analysis. If you want to run the analysis hit ENTER']) 
axis('equal'); 
hold on; 
plot(cropxy(:,2),cropxy(:,3),'+','Color','white') % peaks from bwlabel 
plot(fitxy(:,3),fitxy(:,7),'o','Color','white'); % "good" points 
drawnow 
 
total_progress=1/filenumber; 
 
pause 
 
close all 
fitlength=size(fitxy); 
fitcounter=fitlength(1,1) 
% again for all images 
for m=1:(filenumber-1) % loop through all images 
    tic; %start timer 
    counter=0; 
    I = imread(filenamelist(m,:)); %read image 
    Itemp=mean(double(I),3); 
    I=Itemp; 
    f = waitbar(0,'Working on Image'); 
 
     
    % loop number 
    for c=1:fitcounter %loop trough all points 
        waitbar(c/(fitcounter-1)); %progress bar 
         
        % load variables 
        pointnumber=fitxy(c,(m-1)*12+1); 
        amp_guess_x=fitxy(c,(m-1)*12+2); 
        mu_guess_x=fitxy(c,(m-1)*12+3); 
        sig_guess_x=fitxy(c,(m-1)*12+4); 
        back_guess_x=fitxy(c,(m-1)*12+5); 
        amp_guess_y=fitxy(c,(m-1)*12+6); 
        mu_guess_y=fitxy(c,(m-1)*12+7); 
        sig_guess_y=fitxy(c,(m-1)*12+8); 
        back_guess_y=fitxy(c,(m-1)*12+9); 
        crop_x=fitxy(c,(m-1)*12+10); 
        crop_y=fitxy(c,(m-1)*12+11); 
         
        % crop the area around the point to fit 
         
        cropI=imcrop(I,[(round(mu_guess_x)-round(crop_x)) (round(mu_guess_y)-round(crop_y)) 2*round(crop_x) 2*round(crop_y)]); 
%         cropedI=imcrop(I,[(round(mu_guess_x)-round(crop_x)) (round(mu_guess_y)-round(crop_y)) 2*round(crop_x) 2*round(crop_y)]); 
%         cropI=imsubtract (cropedI, imopen(cropedI,strel('disk',15))); % subtract background 
        %         imshow(cropI) 
        % get line profile in x direction 
        xdatax = [(round(mu_guess_x)-round(crop_x)):1:(round(mu_guess_x)+round(crop_x))]; 
        ydatax=sum(cropI)/(2*(crop_y)); 
        xguessx = [(round(mu_guess_x)-round(crop_x)):0.1:(round(mu_guess_x)+round(crop_x))]; 
        yguessx = (amp_guess_x*exp((-(xguessx-mu_guess_x).^2)./(2.*sig_guess_x.^2))) + back_guess_x; 
        [x,resnormx,residualx,exitflagx,output]  = lsqcurvefit(@gauss_onepk, [amp_guess_x mu_guess_x sig_guess_x back_guess_x], xdatax, ydatax); 
        xtestx = [(round(mu_guess_x)-round(crop_x)):0.1:(round(mu_guess_x)+round(crop_x))]; 
        ytestx = (x(1)*exp((-(xtestx-x(2)).^2)./(2.*x(3).^2))) + x(4); 
%         plot(xdatax,ydatax,'o') 
%         hold on 
%         plot(xtestx,ytestx,'r') 
%         plot(xguessx,yguessx,'b') 
%         title(['Filename: ',filenamelist(m,:), '; Progress [%]: ',num2str((round(total_progress*10))/10), '; Tot. t [h] ', num2str((round(total_time_h*10)/10)), '; Est. t [h] ', num2str((round(estimated_totaltime_h*10)/10))]) 
%         drawnow 
%         hold off 
        xdatay = [(round(mu_guess_y)-round(crop_y)):1:(round(mu_guess_y)+round(crop_y))]; 
        ydatay=sum(cropI')/(2*(crop_y)); 
        xguessy = [(round(mu_guess_y)-round(crop_y)):0.1:(round(mu_guess_y)+round(crop_y))]; 
        yguessy = (amp_guess_y*exp((-(xguessy-mu_guess_y).^2)./(2.*sig_guess_y.^2))) + back_guess_y; 
        [y,resnormy,residualy,exitflagy,output]  = lsqcurvefit(@gauss_onepk, [amp_guess_y mu_guess_y sig_guess_y back_guess_y], xdatay, ydatay); 
        xtesty = [(round(mu_guess_y)-round(crop_y)):0.1:(round(mu_guess_y)+round(crop_y))]; 
        ytesty= (y(1)*exp((-(xtesty-y(2)).^2)./(2.*y(3).^2))) + y(4); 
%         plot(xdatay,ydatay,'o') 
%         hold on 
%         plot(xtesty,ytesty,'g') 
%         plot(xguessy,yguessy,'b') 
%         title(['Filename: ',filenamelist(m,:), '; Progress [%]: ',num2str((round(total_progress*10))/10), '; Tot. t [h] ', num2str((round(total_time_h*10)/10)), '; Est. t [h] ', num2str((round(estimated_totaltime_h*10)/10))]) 
%         drawnow 
%         hold off 
 
         
        if exitflagx>0 
            if exitflagy>0 
                counter=counter+1; 
                fitxy(counter,m*12+1)=pointnumber; 
                fitxy(counter,m*12+2)=abs(x(1)); 
                fitxy(counter,m*12+3)=abs(x(2)); 
                fitxy(counter,m*12+4)=abs(x(3)); 
                fitxy(counter,m*12+5)=abs(x(4)); 
                fitxy(counter,m*12+6)=abs(y(1)); 
                fitxy(counter,m*12+7)=abs(y(2)); 
                fitxy(counter,m*12+8)=abs(y(3)); 
                fitxy(counter,m*12+9)=abs(y(4)); 
                fitxy(counter,m*12+10)=crop_x; 
                fitxy(counter,m*12+11)=crop_y; 
                fitxy(counter,m*12+12)=resnormx; 
                 
            end 
        end 
         
         
    end 
     
    plot(fitxy(:,m*12+1),fitxy(:,m*12+12),'+'); 
    title(['Filename: ',filenamelist(m,:), '; Progress [%]: ',num2str((round(total_progress*10))/10), '; Tot. t [h] ', num2str((round(total_time_h*10)/10)), '; Est. t [h] ', num2str((round(estimated_totaltime_h*10)/10))]) 
    fitcounter=counter; 
    drawnow 
    time(m)=toc; 
    total_time_s=sum(time); 
    total_time_h=sum(time)/3600; 
    image_time_s=total_time_s/m; 
    estimated_totaltime_h=image_time_s*(filenumber)/3600 
    progress_percent=total_time_h/estimated_totaltime_h*100; 
    total_progress=(m+1)/(filenumber)*100 
        close(f); 
 
end   
 
% save the stuff 
save fitxy.dat fitxy -ascii -tabs 
 
[validx,validy]=sortvalidpoints(fitxy); 
title(['Processing Images finished!']) 
 
save('validx'); 
save('validy'); 
 
save validx.dat validx -ascii -tabs; 
save validy.dat validy -ascii -tabs;