/normxcorr/trunk

% 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;