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piv_FFTensemble.m
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piv_FFTensemble.m
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function [xtable, ytable, utable, vtable, typevector,correlation_map] = piv_FFTensemble (autolimit,filepath,video_frame_selection,bg_img_A,bg_img_B,clahe,highp,intenscap,clahesize,highpsize,wienerwurst,wienerwurstsize,roi_inpt,maskiererx,maskierery,interrogationarea,step,subpixfinder,passes,int2,int3,int4,mask_auto,imdeform,repeat,do_pad)
%this funtion performs the PIV analysis. It is a modification of the
%pivFFTmulti, and will do ensemble correlation. That is a suitable
%algorithm for low seeding density as it happens in microPIV.
warning off %#ok<*WNOFF> %MATLAB:log:logOfZero
%% pre-processing is done in this function
result_conv_ensemble = zeros(interrogationarea,interrogationarea); % prepare empty result_conv
if isempty(video_frame_selection) %list with image files was passed
amount_input_imgs=size(filepath,1);
else
amount_input_imgs=numel(video_frame_selection);
end
total_analyses_amount=amount_input_imgs / 2 * passes;
from_total = 0;
tic
skippy=0;
for ensemble_i1=1:2:amount_input_imgs
if isempty(video_frame_selection) %list with image files was passed
%detect if it is b16 or standard pixel image
[~,~,ext] = fileparts(filepath{1});
if strcmp(ext,'.b16')
image1=f_readB16(filepath{ensemble_i1});
image2=f_readB16(filepath{ensemble_i1+1});
else
image1=imread(filepath{ensemble_i1});
image2=imread(filepath{ensemble_i1+1});
end
else % video file was passed
image1 = read(filepath,video_frame_selection(ensemble_i1));
image2 = read(filepath,video_frame_selection(ensemble_i1+1));
end
if size(image1,3)>1
image1=uint8(mean(image1,3));
image2=uint8(mean(image2,3));
%disp('Warning: To optimize speed, your images should be grayscale, 8 bit!')
end
%Subtract background (if existent)
if ~isempty(bg_img_A)
image1=image1-bg_img_A;
end
if ~isempty(bg_img_B)
image2=image2-bg_img_B;
end
%if autolimit == 1 %if autolimit is desired: do autolimit for each image seperately
if size(image1,3)>1
stretcher = stretchlim(rgb2gray(image1));
else
stretcher = stretchlim(image1);
end
minintens1 = stretcher(1);
maxintens1 = stretcher(2);
if size(image2,3)>1
stretcher = stretchlim(rgb2gray(image2));
else
stretcher = stretchlim(image2);
end
minintens2 = stretcher(1);
maxintens2 = stretcher(2);
%end
image1 = PIVlab_preproc (image1,roi_inpt,clahe, clahesize,highp,highpsize,intenscap,wienerwurst,wienerwurstsize,minintens1,maxintens1);
image2 = PIVlab_preproc (image2,roi_inpt,clahe, clahesize,highp,highpsize,intenscap,wienerwurst,wienerwurstsize,minintens2,maxintens2);
if numel(roi_inpt)>0
xroi=roi_inpt(1);
yroi=roi_inpt(2);
widthroi=roi_inpt(3);
heightroi=roi_inpt(4);
image1_roi=double(image1(yroi:yroi+heightroi,xroi:xroi+widthroi));
image2_roi=double(image2(yroi:yroi+heightroi,xroi:xroi+widthroi));
else
xroi=0;
yroi=0;
image1_roi=double(image1);
image2_roi=double(image2);
end
gen_image1_roi = image1_roi;
gen_image2_roi = image2_roi;
%prepare a matrix for calculating the average mask of all images
if ensemble_i1==1
average_mask=zeros(size(image1_roi));
end
%get mask from mask list
ximask={};
yimask={};
if size(maskiererx,2)>=ensemble_i1
for j=1:size(maskiererx,1)
if isempty(maskiererx{j,ensemble_i1})==0
ximask{j,1}=maskiererx{j,ensemble_i1}; %#ok<*AGROW>
yimask{j,1}=maskierery{j,ensemble_i1};
else
break
end
end
if size(ximask,1)>0
mask_inpt=[ximask yimask];
else
mask_inpt=[];
end
else
mask_inpt=[];
end
if numel(mask_inpt)>0
cellmask=mask_inpt;
mask=zeros(size(image1_roi));
for i=1:size(cellmask,1)
masklayerx=cellmask{i,1};
masklayery=cellmask{i,2};
mask = mask + poly2mask(masklayerx-xroi,masklayery-yroi,size(image1_roi,1),size(image1_roi,2)); %kleineres eingangsbild und maske geshiftet
end
else
mask=zeros(size(image1_roi));
end
mask(mask>1)=1;
gen_mask = mask;
try
average_mask=average_mask + mask; %will fail if images are not same dimensions.
catch
cancel = 1;
hgui=getappdata(0,'hgui');
setappdata(hgui, 'cancel', cancel);
text(gca(getappdata(0,'hgui')),10,10,'Error: Image dimensions inconsistent!','color',[1 0 0],'fontsize',20)
drawnow;
break
end
miniy=1+(ceil(interrogationarea/2));
minix=1+(ceil(interrogationarea/2));
maxiy=step*(floor(size(image1_roi,1)/step))-(interrogationarea-1)+(ceil(interrogationarea/2)); %statt size deltax von ROI nehmen
maxix=step*(floor(size(image1_roi,2)/step))-(interrogationarea-1)+(ceil(interrogationarea/2));
numelementsy=floor((maxiy-miniy)/step+1);
numelementsx=floor((maxix-minix)/step+1);
LAy=miniy;
LAx=minix;
LUy=size(image1_roi,1)-maxiy;
LUx=size(image1_roi,2)-maxix;
shift4centery=round((LUy-LAy)/2);
shift4centerx=round((LUx-LAx)/2);
if shift4centery<0 %shift4center will be negative if in the unshifted case the left border is bigger than the right border. the vectormatrix is hence not centered on the image. the matrix cannot be shifted more towards the left border because then image2_crop would have a negative index. The only way to center the matrix would be to remove a column of vectors on the right side. but then we weould have less data....
shift4centery=0;
end
if shift4centerx<0 %shift4center will be negative if in the unshifted case the left border is bigger than the right border. the vectormatrix is hence not centered on the image. the matrix cannot be shifted more towards the left border because then image2_crop would have a negative index. The only way to center the matrix would be to remove a column of vectors on the right side. but then we weould have less data....
shift4centerx=0;
end
miniy=miniy+shift4centery;
minix=minix+shift4centerx;
maxix=maxix+shift4centerx;
maxiy=maxiy+shift4centery;
image1_roi=padarray(image1_roi,[ceil(interrogationarea/2) ceil(interrogationarea/2)], min(min(image1_roi)));
image2_roi=padarray(image2_roi,[ceil(interrogationarea/2) ceil(interrogationarea/2)], min(min(image1_roi)));
mask=padarray(mask,[ceil(interrogationarea/2) ceil(interrogationarea/2)],0);
if (rem(interrogationarea,2) == 0) %for the subpixel displacement measurement
SubPixOffset=1;
else
SubPixOffset=0.5;
end
xtable=zeros(numelementsy,numelementsx);
ytable=xtable; %#ok<*NASGU>
utable=xtable;
vtable=xtable;
typevector=ones(numelementsy,numelementsx);
%% MAINLOOP
try %check if used from GUI
handles=guihandles(getappdata(0,'hgui'));
GUI_avail=1;
hgui=getappdata(0,'hgui');
cancel=getappdata(hgui, 'cancel');
if cancel == 1
break
%disp('user cancelled');
end
catch %#ok<CTCH>
GUI_avail=0;
disp('no GUI')
end
% divide images by small pictures
% new index for image1_roi and image2_roi
s0 = (repmat((miniy:step:maxiy)'-1, 1,numelementsx) + repmat(((minix:step:maxix)-1)*size(image1_roi, 1), numelementsy,1))';
s0 = permute(s0(:), [2 3 1]);
s1 = repmat((1:interrogationarea)',1,interrogationarea) + repmat(((1:interrogationarea)-1)*size(image1_roi, 1),interrogationarea,1);
ss1 = repmat(s1, [1, 1, size(s0,3)])+repmat(s0, [interrogationarea, interrogationarea, 1]);
image1_cut = image1_roi(ss1);
image2_cut = image2_roi(ss1);
if do_pad==1 && passes == 1 %only on first pass
%subtract mean to avoid high frequencies at border of correlation:
image1_cut=image1_cut-mean(image1_cut,[1 2]);
image2_cut=image2_cut-mean(image2_cut,[1 2]);
% padding (faster than padarray) to get the linear correlation:
image1_cut=[image1_cut zeros(interrogationarea,interrogationarea-1,size(image1_cut,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image1_cut,3))];
image2_cut=[image2_cut zeros(interrogationarea,interrogationarea-1,size(image2_cut,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image2_cut,3))];
end
%do fft2:
result_conv = fftshift(fftshift(real(ifft2(conj(fft2(image1_cut)).*fft2(image2_cut))), 1), 2);
if do_pad==1 && passes == 1
%cropping of correlation matrix:
result_conv =result_conv((interrogationarea/2):(3*interrogationarea/2)-1,(interrogationarea/2):(3*interrogationarea/2)-1,:);
end
%% repeated Correlation in the first pass (might make sense to repeat more often to make it even more robust...)
if repeat == 1 && passes == 1
ms=round(step/4); %multishift parameter so groß wie viertel int window
%Shift left bot
s0B = (repmat((miniy+ms:step:maxiy+ms)'-1, 1,numelementsx) + repmat(((minix-ms:step:maxix-ms)-1)*size(image1_roi, 1), numelementsy,1))';
s0B = permute(s0B(:), [2 3 1]);
s1B = repmat((1:interrogationarea)',1,interrogationarea) + repmat(((1:interrogationarea)-1)*size(image1_roi, 1),interrogationarea,1);
ss1B = repmat(s1B, [1, 1, size(s0B,3)])+repmat(s0B, [interrogationarea, interrogationarea, 1]);
image1_cutB = image1_roi(ss1B);
image2_cutB = image2_roi(ss1B);
if do_pad==1 && passes == 1
%subtract mean to avoid high frequencies at border of correlation:
image1_cutB=image1_cutB-mean(image1_cutB,[1 2]);
image2_cutB=image2_cutB-mean(image2_cutB,[1 2]);
% padding (faster than padarray) to get the linear correlation:
image1_cutB=[image1_cutB zeros(interrogationarea,interrogationarea-1,size(image1_cutB,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image1_cutB,3))];
image2_cutB=[image2_cutB zeros(interrogationarea,interrogationarea-1,size(image2_cutB,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image2_cutB,3))];
end
result_convB = fftshift(fftshift(real(ifft2(conj(fft2(image1_cutB)).*fft2(image2_cutB))), 1), 2);
if do_pad==1 && passes == 1
%cropping of correlation matrix:
result_convB =result_convB((interrogationarea/2):(3*interrogationarea/2)-1,(interrogationarea/2):(3*interrogationarea/2)-1,:);
end
%Shift right bot
s0C = (repmat((miniy+ms:step:maxiy+ms)'-1, 1,numelementsx) + repmat(((minix+ms:step:maxix+ms)-1)*size(image1_roi, 1), numelementsy,1))';
s0C = permute(s0C(:), [2 3 1]);
s1C = repmat((1:interrogationarea)',1,interrogationarea) + repmat(((1:interrogationarea)-1)*size(image1_roi, 1),interrogationarea,1);
ss1C = repmat(s1C, [1, 1, size(s0C,3)])+repmat(s0C, [interrogationarea, interrogationarea, 1]);
image1_cutC = image1_roi(ss1C);
image2_cutC = image2_roi(ss1C);
if do_pad==1 && passes == 1
%subtract mean to avoid high frequencies at border of correlation:
image1_cutC=image1_cutC-mean(image1_cutC,[1 2]);
image2_cutC=image2_cutC-mean(image2_cutC,[1 2]);
% padding (faster than padarray) to get the linear correlation:
image1_cutC=[image1_cutC zeros(interrogationarea,interrogationarea-1,size(image1_cutC,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image1_cutC,3))];
image2_cutC=[image2_cutC zeros(interrogationarea,interrogationarea-1,size(image2_cutC,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image2_cutC,3))];
end
result_convC = fftshift(fftshift(real(ifft2(conj(fft2(image1_cutC)).*fft2(image2_cutC))), 1), 2);
if do_pad==1 && passes == 1
%cropping of correlation matrix:
result_convC =result_convC((interrogationarea/2):(3*interrogationarea/2)-1,(interrogationarea/2):(3*interrogationarea/2)-1,:);
end
%Shift left top
s0D = (repmat((miniy-ms:step:maxiy-ms)'-1, 1,numelementsx) + repmat(((minix-ms:step:maxix-ms)-1)*size(image1_roi, 1), numelementsy,1))';
s0D = permute(s0D(:), [2 3 1]);
s1D = repmat((1:interrogationarea)',1,interrogationarea) + repmat(((1:interrogationarea)-1)*size(image1_roi, 1),interrogationarea,1);
ss1D = repmat(s1D, [1, 1, size(s0D,3)])+repmat(s0D, [interrogationarea, interrogationarea, 1]);
image1_cutD = image1_roi(ss1D);
image2_cutD = image2_roi(ss1D);
if do_pad==1 && passes == 1
%subtract mean to avoid high frequencies at border of correlation:
image1_cutD=image1_cutD-mean(image1_cutD,[1 2]);
image2_cutD=image2_cutD-mean(image2_cutD,[1 2]);
% padding (faster than padarray) to get the linear correlation:
image1_cutD=[image1_cutD zeros(interrogationarea,interrogationarea-1,size(image1_cutD,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image1_cutD,3))];
image2_cutD=[image2_cutD zeros(interrogationarea,interrogationarea-1,size(image2_cutD,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image2_cutD,3))];
end
result_convD = fftshift(fftshift(real(ifft2(conj(fft2(image1_cutD)).*fft2(image2_cutD))), 1), 2);
if do_pad==1 && passes == 1
%cropping of correlation matrix:
result_convD =result_convD((interrogationarea/2):(3*interrogationarea/2)-1,(interrogationarea/2):(3*interrogationarea/2)-1,:);
end
%Shift right top
s0E = (repmat((miniy-ms:step:maxiy-ms)'-1, 1,numelementsx) + repmat(((minix+ms:step:maxix+ms)-1)*size(image1_roi, 1), numelementsy,1))';
s0E = permute(s0E(:), [2 3 1]);
s1E = repmat((1:interrogationarea)',1,interrogationarea) + repmat(((1:interrogationarea)-1)*size(image1_roi, 1),interrogationarea,1);
ss1E = repmat(s1E, [1, 1, size(s0E,3)])+repmat(s0E, [interrogationarea, interrogationarea, 1]);
image1_cutE = image1_roi(ss1E);
image2_cutE = image2_roi(ss1E);
if do_pad==1 && passes == 1
%subtract mean to avoid high frequencies at border of correlation:
image1_cutE=image1_cutE-mean(image1_cutE,[1 2]);
image2_cutE=image2_cutE-mean(image2_cutE,[1 2]);
% padding (faster than padarray) to get the linear correlation:
image1_cutE=[image1_cutE zeros(interrogationarea,interrogationarea-1,size(image1_cutE,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image1_cutE,3))];
image2_cutE=[image2_cutE zeros(interrogationarea,interrogationarea-1,size(image2_cutE,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image2_cutE,3))];
end
result_convE = fftshift(fftshift(real(ifft2(conj(fft2(image1_cutE)).*fft2(image2_cutE))), 1), 2);
if do_pad==1 && passes == 1
%cropping of correlation matrix:
result_convE =result_convE((interrogationarea/2):(3*interrogationarea/2)-1,(interrogationarea/2):(3*interrogationarea/2)-1,:);
end
result_conv=result_conv.*result_convB.*result_convC.*result_convD.*result_convE;
end
if mask_auto == 1
%das zentrum der Matrize (3x3) mit dem mittelwert ersetzen = Keine Autokorrelation
%MARKER
h = fspecial('gaussian', 3, 1.5);
h=h/h(2,2);
h=1-h;
%h=repmat(h,1,1,size(result_conv,3));
h=repmat(h,[1,1,size(result_conv,3)]);
h=h.*result_conv((interrogationarea/2)+SubPixOffset-1:(interrogationarea/2)+SubPixOffset+1,(interrogationarea/2)+SubPixOffset-1:(interrogationarea/2)+SubPixOffset+1,:);
result_conv((interrogationarea/2)+SubPixOffset-1:(interrogationarea/2)+SubPixOffset+1,(interrogationarea/2)+SubPixOffset-1:(interrogationarea/2)+SubPixOffset+1,:)=h;
end
%apply mask
ii = find(mask(ss1(round(interrogationarea/2+1), round(interrogationarea/2+1), :)));
result_conv(:,:, ii) = 0;
%average the correlation matrices
try
result_conv_ensemble=result_conv_ensemble+result_conv;
catch % older matlab releases
result_conv_ensemble = zeros(size(result_conv));
result_conv_ensemble=result_conv_ensemble+result_conv;
end
if GUI_avail==1
progri=ensemble_i1/(amount_input_imgs)*100;
from_total=from_total+1;
set(handles.progress, 'string' , ['Pass ' int2str(1) ' progress: ' int2str(progri) '%' ])
set(handles.overall, 'string' , ['Total progress: ' int2str(from_total / total_analyses_amount * 100) '%'])
zeit=toc;
done=from_total;
tocome=total_analyses_amount-done;
zeit=zeit/done*tocome;
hrs=zeit/60^2;
mins=(hrs-floor(hrs))*60;
secs=(mins-floor(mins))*60;
hrs=floor(hrs);
mins=floor(mins);
secs=floor(secs);
set(handles.totaltime,'string', ['Time left: ' sprintf('%2.2d', hrs) 'h ' sprintf('%2.2d', mins) 'm ' sprintf('%2.2d', secs) 's']);
%xxx update display every 10 frames...?
%aber wie, dann müsste man peakfinder machen
if skippy ==0
[xtable,ytable,utable, vtable] = peakfinding (result_conv_ensemble, mask, interrogationarea,minix,step,maxix,miniy,maxiy,SubPixOffset,ss1,subpixfinder);
if verLessThan('matlab','8.4')
delete (findobj(getappdata(0,'hgui'),'type', 'hggroup'))
else
delete (findobj(getappdata(0,'hgui'),'type', 'quiver'))
end
hold on;
vecscale=str2double(get(handles.vectorscale,'string'));
%Problem: wenn colorbar an, z�hlt das auch als aexes...
colorbar('off')
%u_table original gibts nicjt, braichts auch nicht...
quiver ((findobj(getappdata(0,'hgui'),'type', 'axes')),xtable(isnan(utable)==0)+xroi-interrogationarea/2,ytable(isnan(utable)==0)+yroi-interrogationarea/2,utable(isnan(utable)==0)*vecscale,vtable(isnan(utable)==0)*vecscale,'Color', [1-(from_total / total_analyses_amount) (from_total / total_analyses_amount) 0.15],'autoscale','off')
%quiver ((findobj(getappdata(0,'hgui'),'type', 'axes')),xtable(isnan(utable)==1)+xroi-interrogationarea/2,ytable(isnan(utable)==1)+yroi-interrogationarea/2,utable(isnan(utable)==1)*vecscale,vtable(isnan(utable)==1)*vecscale,'Color',[0.7 0.15 0.15], 'autoscale','off')
hold off
drawnow;
end
if skippy <10
skippy=skippy+1;
else
skippy=0;
end
try
drawnow limitrate
catch
drawnow
end
else
fprintf('.');
end
if passes==1 % only 1 pass selected, so correlation coefficient will be calculated in this (first & final) pass.
if ensemble_i1==1 %first image pair
correlation_map=zeros(size(typevector));
corr_map_cnt=0;
end
for cor_i=1:size(image1_cut,3)
correlation_map(cor_i)=correlation_map(cor_i) + corr2(image1_cut(:,:,cor_i),image2_cut(:,:,cor_i));
end
corr_map_cnt=corr_map_cnt+1;
end
end
%correlation_map=[];
if cancel == 0
%% Correlation matrix of pass 1 is done.
[xtable,ytable,utable, vtable] = peakfinding (result_conv_ensemble, mask, interrogationarea,minix,step,maxix,miniy,maxiy,SubPixOffset,ss1,subpixfinder);
for multipass=1:passes-1
% unfortunately, preprocessing has to be done again for every pass, otherwise i would have to save the modified data somehow.
if multipass==1
interrogationarea=round(int2/2)*2;
end
if multipass==2
interrogationarea=round(int3/2)*2;
end
if multipass==3
interrogationarea=round(int4/2)*2;
end
result_conv_ensemble = zeros(interrogationarea,interrogationarea); % prepare empty result_conv
skippy=0;
for ensemble_i1=1:2:amount_input_imgs
if skippy <10
skippy=skippy+1;
else
skippy=0;
end
if isempty(video_frame_selection) %list with image files was passed
if strcmp(ext,'.b16')
image1=f_readB16(filepath{ensemble_i1});
image2=f_readB16(filepath{ensemble_i1+1});
else
image1=imread(filepath{ensemble_i1});
image2=imread(filepath{ensemble_i1+1});
end
else % video file was passed
image1 = read(filepath,video_frame_selection(ensemble_i1));
image2 = read(filepath,video_frame_selection(ensemble_i1+1));
end
if size(image1,3)>1
image1=uint8(mean(image1,3));
image2=uint8(mean(image2,3));
end
%subtract bg if present
if ~isempty(bg_img_A)
image1=image1-bg_img_A;
end
if ~isempty(bg_img_B)
image2=image2-bg_img_B;
end
%if autolimit == 1 %if autolimit is desired: do autolimit for each image seperately
if size(image1,3)>1
stretcher = stretchlim(rgb2gray(image1));
else
stretcher = stretchlim(image1);
end
minintens1 = stretcher(1);
maxintens1 = stretcher(2);
if size(image2,3)>1
stretcher = stretchlim(rgb2gray(image2));
else
stretcher = stretchlim(image2);
end
minintens2 = stretcher(1);
maxintens2 = stretcher(2);
%end
image1 = PIVlab_preproc (image1,roi_inpt,clahe, clahesize,highp,highpsize,intenscap,wienerwurst,wienerwurstsize,minintens1,maxintens1);
image2 = PIVlab_preproc (image2,roi_inpt,clahe, clahesize,highp,highpsize,intenscap,wienerwurst,wienerwurstsize,minintens2,maxintens2);
if numel(roi_inpt)>0
xroi=roi_inpt(1);
yroi=roi_inpt(2);
widthroi=roi_inpt(3);
heightroi=roi_inpt(4);
image1_roi=double(image1(yroi:yroi+heightroi,xroi:xroi+widthroi));
image2_roi=double(image2(yroi:yroi+heightroi,xroi:xroi+widthroi));
else
xroi=0;
yroi=0;
image1_roi=double(image1);
image2_roi=double(image2);
end
gen_image1_roi = image1_roi;
gen_image2_roi = image2_roi;
if GUI_avail==1
progri=ensemble_i1/(amount_input_imgs)*100;
from_total=from_total+1;
set(handles.progress, 'string' , ['Pass ' int2str(multipass+1) ' progress: ' int2str(progri) '%' ])
set(handles.overall, 'string' , ['Total progress: ' int2str(from_total / total_analyses_amount * 100) '%'])
zeit=toc;
done=from_total;
tocome=total_analyses_amount-done;
zeit=zeit/done*tocome;
hrs=zeit/60^2;
mins=(hrs-floor(hrs))*60;
secs=(mins-floor(mins))*60;
hrs=floor(hrs);
mins=floor(mins);
secs=floor(secs);
set(handles.totaltime,'string', ['Time left: ' sprintf('%2.2d', hrs) 'h ' sprintf('%2.2d', mins) 'm ' sprintf('%2.2d', secs) 's']);
try
drawnow limitrate
catch
drawnow
end
else
fprintf('.');
end
%multipass validation, smoothing
utable_orig=utable;
vtable_orig=vtable;
[utable,vtable] = PIVlab_postproc (utable,vtable,[],[], [], 1,4, 1,1.5);
if GUI_avail==1
cancel=getappdata(hgui, 'cancel');
if cancel == 1
break
%disp('user cancelled');
end
if skippy ==0
if verLessThan('matlab','8.4')
delete (findobj(getappdata(0,'hgui'),'type', 'hggroup'))
else
delete (findobj(getappdata(0,'hgui'),'type', 'quiver'))
end
hold on;
vecscale=str2double(get(handles.vectorscale,'string'));
%Problem: wenn colorbar an, z�hlt das auch als aexes...
colorbar('off')
quiver ((findobj(getappdata(0,'hgui'),'type', 'axes')),xtable(isnan(utable)==0)+xroi-interrogationarea/2,ytable(isnan(utable)==0)+yroi-interrogationarea/2,utable(isnan(utable)==0)*vecscale,vtable(isnan(utable)==0)*vecscale,'Color', [1-(from_total / total_analyses_amount) (from_total / total_analyses_amount) 0.15],'autoscale','off')
% quiver ((findobj(getappdata(0,'hgui'),'type', 'axes')),xtable(isnan(utable)==0)+xroi-interrogationarea/2,ytable(isnan(utable)==0)+yroi-interrogationarea/2,utable_orig(isnan(utable)==0)*vecscale,vtable_orig(isnan(utable)==0)*vecscale,'Color', [0.15 0.7 0.15],'autoscale','off')
%quiver ((findobj(getappdata(0,'hgui'),'type', 'axes')),xtable(isnan(utable)==1)+xroi-interrogationarea/2,ytable(isnan(utable)==1)+yroi-interrogationarea/2,utable_orig(isnan(utable)==1)*vecscale,vtable_orig(isnan(utable)==1)*vecscale,'Color',[0.7 0.15 0.15], 'autoscale','off')
drawnow
hold off
end
end
%replace nans
utable=inpaint_nans(utable,4);
vtable=inpaint_nans(vtable,4);
%smooth predictor
try
if multipass<passes-1
utable = smoothn(utable,0.6); %stronger smoothing for first passes
vtable = smoothn(vtable,0.6);
else
utable = smoothn(utable); %weaker smoothing for last pass
vtable = smoothn(vtable);
end
catch
%old matlab versions: gaussian kernel
h=fspecial('gaussian',5,1);
utable=imfilter(utable,h,'replicate');
vtable=imfilter(vtable,h,'replicate');
end
if multipass==1
interrogationarea=round(int2/2)*2;
end
if multipass==2
interrogationarea=round(int3/2)*2;
end
if multipass==3
interrogationarea=round(int4/2)*2;
end
step=interrogationarea/2;
%bildkoordinaten neu errechnen:
image1_roi = gen_image1_roi;
image2_roi = gen_image2_roi;
%get mask from mask list
ximask={};
yimask={};
if size(maskiererx,2)>=ensemble_i1
for j=1:size(maskiererx,1)
if isempty(maskiererx{j,ensemble_i1})==0
ximask{j,1}=maskiererx{j,ensemble_i1}; %#ok<*AGROW>
yimask{j,1}=maskierery{j,ensemble_i1};
else
break
end
end
if size(ximask,1)>0
mask_inpt=[ximask yimask];
else
mask_inpt=[];
end
else
mask_inpt=[];
end
if numel(mask_inpt)>0
cellmask=mask_inpt;
mask=zeros(size(image1_roi));
for i=1:size(cellmask,1)
masklayerx=cellmask{i,1};
masklayery=cellmask{i,2};
mask = mask + poly2mask(masklayerx-xroi,masklayery-yroi,size(image1_roi,1),size(image1_roi,2)); %kleineres eingangsbild und maske geshiftet
end
else
mask=zeros(size(image1_roi));
end
mask(mask>1)=1;
gen_mask = mask;
miniy=1+(ceil(interrogationarea/2));
minix=1+(ceil(interrogationarea/2));
maxiy=step*(floor(size(image1_roi,1)/step))-(interrogationarea-1)+(ceil(interrogationarea/2)); %statt size deltax von ROI nehmen
maxix=step*(floor(size(image1_roi,2)/step))-(interrogationarea-1)+(ceil(interrogationarea/2));
numelementsy=floor((maxiy-miniy)/step+1);
numelementsx=floor((maxix-minix)/step+1);
LAy=miniy;
LAx=minix;
LUy=size(image1_roi,1)-maxiy;
LUx=size(image1_roi,2)-maxix;
shift4centery=round((LUy-LAy)/2);
shift4centerx=round((LUx-LAx)/2);
if shift4centery<0 %shift4center will be negative if in the unshifted case the left border is bigger than the right border. the vectormatrix is hence not centered on the image. the matrix cannot be shifted more towards the left border because then image2_crop would have a negative index. The only way to center the matrix would be to remove a column of vectors on the right side. but then we weould have less data....
shift4centery=0;
end
if shift4centerx<0 %shift4center will be negative if in the unshifted case the left border is bigger than the right border. the vectormatrix is hence not centered on the image. the matrix cannot be shifted more towards the left border because then image2_crop would have a negative index. The only way to center the matrix would be to remove a column of vectors on the right side. but then we weould have less data....
shift4centerx=0;
end
miniy=miniy+shift4centery;
minix=minix+shift4centerx;
maxix=maxix+shift4centerx;
maxiy=maxiy+shift4centery;
image1_roi=padarray(image1_roi,[ceil(interrogationarea/2) ceil(interrogationarea/2)], min(min(image1_roi)));
image2_roi=padarray(image2_roi,[ceil(interrogationarea/2) ceil(interrogationarea/2)], min(min(image1_roi)));
mask=padarray(mask,[ceil(interrogationarea/2) ceil(interrogationarea/2)],0);
if (rem(interrogationarea,2) == 0) %for the subpixel displacement measurement
SubPixOffset=1;
else
SubPixOffset=0.5;
end
xtable_old=xtable;
ytable_old=ytable;
typevector=ones(numelementsy,numelementsx);
xtable = repmat((minix:step:maxix), numelementsy, 1) + interrogationarea/2;
ytable = repmat((miniy:step:maxiy)', 1, numelementsx) + interrogationarea/2;
%xtable alt und neu geben koordinaten wo die vektoren herkommen.
%d.h. u und v auf die gew�nschte gr��e bringen+interpolieren
utable=interp2(xtable_old,ytable_old,utable,xtable,ytable,'*spline');
vtable=interp2(xtable_old,ytable_old,vtable,xtable,ytable,'*spline');
utable_1= padarray(utable, [1,1], 'replicate');
vtable_1= padarray(vtable, [1,1], 'replicate');
%add 1 line around image for border regions... linear extrap
firstlinex=xtable(1,:);
firstlinex_intp=interp1(1:1:size(firstlinex,2),firstlinex,0:1:size(firstlinex,2)+1,'linear','extrap');
xtable_1=repmat(firstlinex_intp,size(xtable,1)+2,1);
firstliney=ytable(:,1);
firstliney_intp=interp1(1:1:size(firstliney,1),firstliney,0:1:size(firstliney,1)+1,'linear','extrap')';
ytable_1=repmat(firstliney_intp,1,size(ytable,2)+2);
X=xtable_1; %original locations of vectors in whole image
Y=ytable_1;
U=utable_1; %interesting portion of u
V=vtable_1; % "" of v
X1=X(1,1):1:X(1,end)-1;
Y1=(Y(1,1):1:Y(end,1)-1)';
X1=repmat(X1,size(Y1, 1),1);
Y1=repmat(Y1,1,size(X1, 2));
U1 = interp2(X,Y,U,X1,Y1,'*linear');
V1 = interp2(X,Y,V,X1,Y1,'*linear');
image2_crop_i1 = interp2(1:size(image2_roi,2),(1:size(image2_roi,1))',double(image2_roi),X1+U1,Y1+V1,imdeform); %linear is 3x faster and looks ok...
xb = find(X1(1,:) == xtable_1(1,1));
yb = find(Y1(:,1) == ytable_1(1,1));
% divide images by small pictures
% new index for image1_roi
s0 = (repmat((miniy:step:maxiy)'-1, 1,numelementsx) + repmat(((minix:step:maxix)-1)*size(image1_roi, 1), numelementsy,1))';
s0 = permute(s0(:), [2 3 1]);
s1 = repmat((1:interrogationarea)',1,interrogationarea) + repmat(((1:interrogationarea)-1)*size(image1_roi, 1),interrogationarea,1);
ss1 = repmat(s1, [1, 1, size(s0,3)]) + repmat(s0, [interrogationarea, interrogationarea, 1]);
% new index for image2_crop_i1
s0 = (repmat(yb-step+step*(1:numelementsy)'-1, 1,numelementsx) + repmat((xb-step+step*(1:numelementsx)-1)*size(image2_crop_i1, 1), numelementsy,1))';
s0 = permute(s0(:), [2 3 1]) - s0(1);
s2 = repmat((1:2*step)',1,2*step) + repmat(((1:2*step)-1)*size(image2_crop_i1, 1),2*step,1);
ss2 = repmat(s2, [1, 1, size(s0,3)]) + repmat(s0, [interrogationarea, interrogationarea, 1]);
image1_cut = image1_roi(ss1);
image2_cut = image2_crop_i1(ss2);
if do_pad==1 && multipass==passes-1
%subtract mean to avoid high frequencies at border of correlation:
try
image1_cut=image1_cut-mean(image1_cut,[1 2]);
image2_cut=image2_cut-mean(image2_cut,[1 2]);
catch
for oldmatlab=1:size(image1_cut,3);
image1_cut(:,:,oldmatlab)=image1_cut(:,:,oldmatlab)-mean(mean(image1_cut(:,:,oldmatlab)));
image2_cut(:,:,oldmatlab)=image2_cut(:,:,oldmatlab)-mean(mean(image2_cut(:,:,oldmatlab)));
end
end
% padding (faster than padarray) to get the linear correlation:
image1_cut=[image1_cut zeros(interrogationarea,interrogationarea-1,size(image1_cut,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image1_cut,3))];
image2_cut=[image2_cut zeros(interrogationarea,interrogationarea-1,size(image2_cut,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image2_cut,3))];
end
%do fft2:
result_conv = fftshift(fftshift(real(ifft2(conj(fft2(image1_cut)).*fft2(image2_cut))), 1), 2);
if do_pad==1 && multipass==passes-1
%cropping of correlation matrix:
result_conv =result_conv((interrogationarea/2):(3*interrogationarea/2)-1,(interrogationarea/2):(3*interrogationarea/2)-1,:);
end
%% repeated correlation
if repeat == 1 && multipass==passes-1
ms=round(step/4); %multishift parameter so groß wie viertel int window
%Shift left bot
image2_crop_i1 = interp2(1:size(image2_roi,2),(1:size(image2_roi,1))',double(image2_roi),X1+U1-ms,Y1+V1+ms,imdeform); %linear is 3x faster and looks ok...
xb = find(X1(1,:) == xtable_1(1,1));
yb = find(Y1(:,1) == ytable_1(1,1));
s0 = (repmat((miniy+ms:step:maxiy+ms)'-1, 1,numelementsx) + repmat(((minix-ms:step:maxix-ms)-1)*size(image1_roi, 1), numelementsy,1))';
s0 = permute(s0(:), [2 3 1]);
s1 = repmat((1:interrogationarea)',1,interrogationarea) + repmat(((1:interrogationarea)-1)*size(image1_roi, 1),interrogationarea,1);
ss1 = repmat(s1, [1, 1, size(s0,3)]) + repmat(s0, [interrogationarea, interrogationarea, 1]);
s0 = (repmat(yb-step+step*(1:numelementsy)'-1, 1,numelementsx) + repmat((xb-step+step*(1:numelementsx)-1)*size(image2_crop_i1, 1), numelementsy,1))';
s0 = permute(s0(:), [2 3 1]) - s0(1);
s2 = repmat((1:2*step)',1,2*step) + repmat(((1:2*step)-1)*size(image2_crop_i1, 1),2*step,1);
ss2 = repmat(s2, [1, 1, size(s0,3)]) + repmat(s0, [interrogationarea, interrogationarea, 1]);
image1_cut = image1_roi(ss1);
image2_cut = image2_crop_i1(ss2);
if do_pad==1 && multipass==passes-1
%subtract mean to avoid high frequencies at border of correlation:
try
image1_cut=image1_cut-mean(image1_cut,[1 2]);
image2_cut=image2_cut-mean(image2_cut,[1 2]);
catch
for oldmatlab=1:size(image1_cut,3);
image1_cut(:,:,oldmatlab)=image1_cut(:,:,oldmatlab)-mean(mean(image1_cut(:,:,oldmatlab)));
image2_cut(:,:,oldmatlab)=image2_cut(:,:,oldmatlab)-mean(mean(image2_cut(:,:,oldmatlab)));
end
end
% padding (faster than padarray) to get the linear correlation:
image1_cut=[image1_cut zeros(interrogationarea,interrogationarea-1,size(image1_cut,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image1_cut,3))];
image2_cut=[image2_cut zeros(interrogationarea,interrogationarea-1,size(image2_cut,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image2_cut,3))];
end
result_convB = fftshift(fftshift(real(ifft2(conj(fft2(image1_cut)).*fft2(image2_cut))), 1), 2);
if do_pad==1 && multipass==passes-1
%cropping of correlation matrix:
result_convB =result_convB((interrogationarea/2):(3*interrogationarea/2)-1,(interrogationarea/2):(3*interrogationarea/2)-1,:);
end
%Shift right bot
image2_crop_i1 = interp2(1:size(image2_roi,2),(1:size(image2_roi,1))',double(image2_roi),X1+U1+ms,Y1+V1+ms,imdeform); %linear is 3x faster and looks ok...
xb = find(X1(1,:) == xtable_1(1,1));
yb = find(Y1(:,1) == ytable_1(1,1));
s0 = (repmat((miniy+ms:step:maxiy+ms)'-1, 1,numelementsx) + repmat(((minix+ms:step:maxix+ms)-1)*size(image1_roi, 1), numelementsy,1))';
s0 = permute(s0(:), [2 3 1]);
s1 = repmat((1:interrogationarea)',1,interrogationarea) + repmat(((1:interrogationarea)-1)*size(image1_roi, 1),interrogationarea,1);
ss1 = repmat(s1, [1, 1, size(s0,3)]) + repmat(s0, [interrogationarea, interrogationarea, 1]);
s0 = (repmat(yb-step+step*(1:numelementsy)'-1, 1,numelementsx) + repmat((xb-step+step*(1:numelementsx)-1)*size(image2_crop_i1, 1), numelementsy,1))';
s0 = permute(s0(:), [2 3 1]) - s0(1);
s2 = repmat((1:2*step)',1,2*step) + repmat(((1:2*step)-1)*size(image2_crop_i1, 1),2*step,1);
ss2 = repmat(s2, [1, 1, size(s0,3)]) + repmat(s0, [interrogationarea, interrogationarea, 1]);
image1_cut = image1_roi(ss1);
image2_cut = image2_crop_i1(ss2);
if do_pad==1 && multipass==passes-1
%subtract mean to avoid high frequencies at border of correlation:
try
image1_cut=image1_cut-mean(image1_cut,[1 2]);
image2_cut=image2_cut-mean(image2_cut,[1 2]);
catch
for oldmatlab=1:size(image1_cut,3);
image1_cut(:,:,oldmatlab)=image1_cut(:,:,oldmatlab)-mean(mean(image1_cut(:,:,oldmatlab)));
image2_cut(:,:,oldmatlab)=image2_cut(:,:,oldmatlab)-mean(mean(image2_cut(:,:,oldmatlab)));
end
end
% padding (faster than padarray) to get the linear correlation:
image1_cut=[image1_cut zeros(interrogationarea,interrogationarea-1,size(image1_cut,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image1_cut,3))];
image2_cut=[image2_cut zeros(interrogationarea,interrogationarea-1,size(image2_cut,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image2_cut,3))];
end
result_convC = fftshift(fftshift(real(ifft2(conj(fft2(image1_cut)).*fft2(image2_cut))), 1), 2);
if do_pad==1 && multipass==passes-1
%cropping of correlation matrix:
result_convC =result_convC((interrogationarea/2):(3*interrogationarea/2)-1,(interrogationarea/2):(3*interrogationarea/2)-1,:);
end
%Shift left top
image2_crop_i1 = interp2(1:size(image2_roi,2),(1:size(image2_roi,1))',double(image2_roi),X1+U1-ms,Y1+V1-ms,imdeform); %linear is 3x faster and looks ok...
xb = find(X1(1,:) == xtable_1(1,1));
yb = find(Y1(:,1) == ytable_1(1,1));
s0 = (repmat((miniy-ms:step:maxiy-ms)'-1, 1,numelementsx) + repmat(((minix-ms:step:maxix-ms)-1)*size(image1_roi, 1), numelementsy,1))';
s0 = permute(s0(:), [2 3 1]);
s1 = repmat((1:interrogationarea)',1,interrogationarea) + repmat(((1:interrogationarea)-1)*size(image1_roi, 1),interrogationarea,1);
ss1 = repmat(s1, [1, 1, size(s0,3)]) + repmat(s0, [interrogationarea, interrogationarea, 1]);
s0 = (repmat(yb-step+step*(1:numelementsy)'-1, 1,numelementsx) + repmat((xb-step+step*(1:numelementsx)-1)*size(image2_crop_i1, 1), numelementsy,1))';
s0 = permute(s0(:), [2 3 1]) - s0(1);
s2 = repmat((1:2*step)',1,2*step) + repmat(((1:2*step)-1)*size(image2_crop_i1, 1),2*step,1);
ss2 = repmat(s2, [1, 1, size(s0,3)]) + repmat(s0, [interrogationarea, interrogationarea, 1]);
image1_cut = image1_roi(ss1);
image2_cut = image2_crop_i1(ss2);
if do_pad==1 && multipass==passes-1
%subtract mean to avoid high frequencies at border of correlation:
try
image1_cut=image1_cut-mean(image1_cut,[1 2]);
image2_cut=image2_cut-mean(image2_cut,[1 2]);
catch
for oldmatlab=1:size(image1_cut,3)
image1_cut(:,:,oldmatlab)=image1_cut(:,:,oldmatlab)-mean(mean(image1_cut(:,:,oldmatlab)));
image2_cut(:,:,oldmatlab)=image2_cut(:,:,oldmatlab)-mean(mean(image2_cut(:,:,oldmatlab)));
end
end
% padding (faster than padarray) to get the linear correlation:
image1_cut=[image1_cut zeros(interrogationarea,interrogationarea-1,size(image1_cut,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image1_cut,3))];
image2_cut=[image2_cut zeros(interrogationarea,interrogationarea-1,size(image2_cut,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image2_cut,3))];
end
result_convD = fftshift(fftshift(real(ifft2(conj(fft2(image1_cut)).*fft2(image2_cut))), 1), 2);
if do_pad==1 && multipass==passes-1
%cropping of correlation matrix:
result_convD =result_convD((interrogationarea/2):(3*interrogationarea/2)-1,(interrogationarea/2):(3*interrogationarea/2)-1,:);
end
%Shift right top
image2_crop_i1 = interp2(1:size(image2_roi,2),(1:size(image2_roi,1))',double(image2_roi),X1+U1+ms,Y1+V1-ms,imdeform); %linear is 3x faster and looks ok...
xb = find(X1(1,:) == xtable_1(1,1));
yb = find(Y1(:,1) == ytable_1(1,1));
s0 = (repmat((miniy-ms:step:maxiy-ms)'-1, 1,numelementsx) + repmat(((minix+ms:step:maxix+ms)-1)*size(image1_roi, 1), numelementsy,1))';
s0 = permute(s0(:), [2 3 1]);
s1 = repmat((1:interrogationarea)',1,interrogationarea) + repmat(((1:interrogationarea)-1)*size(image1_roi, 1),interrogationarea,1);
ss1 = repmat(s1, [1, 1, size(s0,3)]) + repmat(s0, [interrogationarea, interrogationarea, 1]);
s0 = (repmat(yb-step+step*(1:numelementsy)'-1, 1,numelementsx) + repmat((xb-step+step*(1:numelementsx)-1)*size(image2_crop_i1, 1), numelementsy,1))';
s0 = permute(s0(:), [2 3 1]) - s0(1);
s2 = repmat((1:2*step)',1,2*step) + repmat(((1:2*step)-1)*size(image2_crop_i1, 1),2*step,1);
ss2 = repmat(s2, [1, 1, size(s0,3)]) + repmat(s0, [interrogationarea, interrogationarea, 1]);
image1_cut = image1_roi(ss1);
image2_cut = image2_crop_i1(ss2);
if do_pad==1 && multipass==passes-1
%subtract mean to avoid high frequencies at border of correlation:
try
image1_cut=image1_cut-mean(image1_cut,[1 2]);
image2_cut=image2_cut-mean(image2_cut,[1 2]);
catch
for oldmatlab=1:size(image1_cut,3)
image1_cut(:,:,oldmatlab)=image1_cut(:,:,oldmatlab)-mean(mean(image1_cut(:,:,oldmatlab)));
image2_cut(:,:,oldmatlab)=image2_cut(:,:,oldmatlab)-mean(mean(image2_cut(:,:,oldmatlab)));
end
end
% padding (faster than padarray) to get the linear correlation:
image1_cut=[image1_cut zeros(interrogationarea,interrogationarea-1,size(image1_cut,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image1_cut,3))];
image2_cut=[image2_cut zeros(interrogationarea,interrogationarea-1,size(image2_cut,3)); zeros(interrogationarea-1,2*interrogationarea-1,size(image2_cut,3))];
end
result_convE = fftshift(fftshift(real(ifft2(conj(fft2(image1_cut)).*fft2(image2_cut))), 1), 2);
if do_pad==1 && multipass==passes-1
%cropping of correlation matrix:
result_convE =result_convE((interrogationarea/2):(3*interrogationarea/2)-1,(interrogationarea/2):(3*interrogationarea/2)-1,:);
end
result_conv=result_conv.*result_convB.*result_convC.*result_convD.*result_convE;
end
if mask_auto == 1
%limit peak search arena....
emptymatrix=zeros(size(result_conv,1),size(result_conv,2),size(result_conv,3));
%emptymatrix=emptymatrix+0.1;
if interrogationarea > 8 % masking central peak will not work for extrmely small interrogation areas. And it also doesn't make sense.
sizeones=4;
%h = fspecial('gaussian', sizeones*2+1,1);
h=fspecial('disk',4);
h=h/max(max(h));
%h=repmat(h,1,1,size(result_conv,3));
h=repmat(h,[1,1,size(result_conv,3)]);
emptymatrix((interrogationarea/2)+SubPixOffset-sizeones:(interrogationarea/2)+SubPixOffset+sizeones,(interrogationarea/2)+SubPixOffset-sizeones:(interrogationarea/2)+SubPixOffset+sizeones,:)=h;
result_conv = result_conv .* emptymatrix;
else
disp('All interrogation areas must be larger than 8 pixels for disabling auto correlation successfully.')
end
end
%apply mask ---
ii = find(mask(ss1(round(interrogationarea/2+1), round(interrogationarea/2+1), :)));
result_conv(:,:, ii) = 0;
%add alle result_conv
try
result_conv_ensemble=result_conv_ensemble+result_conv;
catch % older matlab releases
result_conv_ensemble = zeros(size(result_conv));
result_conv_ensemble=result_conv_ensemble+result_conv;
end
if multipass==passes-1 %correlation strength only in last pass
if ensemble_i1==1 %first image pair
correlation_map=zeros(size(typevector));
corr_map_cnt=0;
end
%Correlation strength
for cor_i=1:size(image1_cut,3)
correlation_map(cor_i)=correlation_map(cor_i)+corr2(image1_cut(:,:,cor_i),image2_cut(:,:,cor_i));
end
corr_map_cnt=corr_map_cnt+1;
end
end
[xtable,ytable,utable2, vtable2] = peakfinding (result_conv_ensemble, [], interrogationarea,minix,step,maxix,miniy,maxiy,SubPixOffset,ss1,subpixfinder);
utable = utable+utable2;
vtable = vtable+vtable2;
end
if cancel == 0
%mask only if all frames are masked
%apply mask
nrx=0;
nrxreal=0;
nry=0;
average_mask=padarray(average_mask,[ceil(interrogationarea/2) ceil(interrogationarea/2)],0);
for jmask = miniy:step:maxiy %vertical loop
nry=nry+1;
for imask = minix:step:maxix % horizontal loop
nrx=nrx+1;%used to determine the pos of the vector in resulting matrix
if nrxreal < numelementsx
nrxreal=nrxreal+1;
else
nrxreal=1;
end
%fehlerzeile:
if average_mask(round(jmask+interrogationarea/2),round(imask+interrogationarea/2)) >= amount_input_imgs/2
typevector(nry,nrxreal)=0;
end
end
end
xtable=xtable-ceil(interrogationarea/2);
ytable=ytable-ceil(interrogationarea/2);
xtable=xtable+xroi;
ytable=ytable+yroi;
end
%% Write correlation matrices to the workspace
%{
try
counter=evalin('base','counter');
counter=counter+1;
assignin('base','counter',counter);
all_matrices=evalin('base','all_matrices');
all_matrices{end+1}=result_conv_ensemble;
assignin('base','all_matrices',all_matrices);
disp('appended matrix')
catch
assignin('base','counter',1);
all_matrices{1}=result_conv_ensemble;
assignin('base','all_matrices',all_matrices);
disp('created new matrix')
end
%}
correlation_map = permute(reshape(correlation_map, [size(xtable')]), [2 1 3])/corr_map_cnt;
%clear Correlation map in masked area
correlation_map(typevector==0) = 0;
end
function [xtable,ytable,utable, vtable] = peakfinding (result_conv_ensemble, mask, interrogationarea,minix,step,maxix,miniy,maxiy,SubPixOffset,ss1,subpixfinder)
minres = permute(repmat(squeeze(min(min(result_conv_ensemble))), [1, size(result_conv_ensemble, 1), size(result_conv_ensemble, 2)]), [2 3 1]);
deltares = permute(repmat(squeeze(max(max(result_conv_ensemble))-min(min(result_conv_ensemble))),[ 1, size(result_conv_ensemble, 1), size(result_conv_ensemble, 2)]), [2 3 1]);
result_conv_ensemble = ((result_conv_ensemble-minres)./deltares)*255;
%apply mask ---
if isempty (mask)==0
ii = find(mask(ss1(round(interrogationarea/2+1), round(interrogationarea/2+1), :)));
result_conv_ensemble(:,:, ii) = 0;
end
[y, x, z] = ind2sub(size(result_conv_ensemble), find(result_conv_ensemble==255));
% we need only one peak from each couple pictures
[z1, zi] = sort(z);
dz1 = [z1(1); diff(z1)];
i0 = find(dz1~=0);
x1 = x(zi(i0));
y1 = y(zi(i0));
z1 = z(zi(i0));
xtable = repmat((minix:step:maxix)+interrogationarea/2, length(miniy:step:maxiy), 1);
ytable = repmat(((miniy:step:maxiy)+interrogationarea/2)', 1, length(minix:step:maxix));
if subpixfinder==1
[vector] = SUBPIXGAUSS (result_conv_ensemble,interrogationarea, x1, y1, z1, SubPixOffset);
elseif subpixfinder==2
[vector] = SUBPIX2DGAUSS (result_conv_ensemble,interrogationarea, x1, y1, z1, SubPixOffset);
end
vector = permute(reshape(vector, [size(xtable') 2]), [2 1 3]);
utable = vector(:,:,1);
vtable = vector(:,:,2);
function [vector] = SUBPIXGAUSS(result_conv, interrogationarea, x, y, z, SubPixOffset)
%was hat peak nr.1 für einen Durchmesser?
%figure;imagesc((1-im2bw(uint8(result_conv(:,:,155)),0.9)).*result_conv(:,:,101))
xi = find(~((x <= (size(result_conv,2)-1)) & (y <= (size(result_conv,1)-1)) & (x >= 2) & (y >= 2)));
x(xi) = [];
y(xi) = [];
z(xi) = [];
xmax = size(result_conv, 2);
vector = NaN(size(result_conv,3), 2);
if(numel(x)~=0)
ip = sub2ind(size(result_conv), y, x, z);
%the following 8 lines are copyright (c) 1998, Uri Shavit, Roi Gurka, Alex Liberzon, Technion � Israel Institute of Technology
%https://urapiv.wordpress.com
f0 = log(result_conv(ip));
f1 = log(result_conv(ip-1));
f2 = log(result_conv(ip+1));
peaky = y + (f1-f2)./(2*f1-4*f0+2*f2);
f0 = log(result_conv(ip));
f1 = log(result_conv(ip-xmax));
f2 = log(result_conv(ip+xmax));
peakx = x + (f1-f2)./(2*f1-4*f0+2*f2);
SubpixelX=peakx-(interrogationarea/2)-SubPixOffset;
SubpixelY=peaky-(interrogationarea/2)-SubPixOffset;
vector(z, :) = [SubpixelX, SubpixelY];
end
function [vector] = SUBPIX2DGAUSS(result_conv, interrogationarea, x, y, z, SubPixOffset)
xi = find(~((x <= (size(result_conv,2)-1)) & (y <= (size(result_conv,1)-1)) & (x >= 2) & (y >= 2)));
x(xi) = [];
y(xi) = [];
z(xi) = [];
xmax = size(result_conv, 2);
vector = NaN(size(result_conv,3), 2);