Skip to content

/ Moment_of_Correlation_2DPIV_uncertainty

Permalink
Browse files

Add files via upload 

adding main MC and IM uncertainty function
  • Loading branch information
@bhattac3
bhattac3 committed Apr 30, 2021
1 parent a296dcf commit d952b96c2d4e088895a3a67bf5e4fe4dcb4a2c31
Unified Split
Showing with 494 additions and 0 deletions.
  1. +236 −0 Moment_of_correlation.m
  2. +258 −0 image_matching_prana.m
236 Moment_of_correlation.m
View file
@@ -0,0 +1,236 @@
function[Ixx,Iyy,biasx,biasy,Neff,Autod]=Moment_of_correlation(P21,f1,f2,Sx,Sy,cnorm,D,fftindx,fftindy,G,DXtemp,DYtemp,region1,region2,MIest)
%% This function calculates the standard uncertainty from the cross-correlation plane using Moment of Correlation method.
% written by Sayantan Bhattacharya

% Inputs
%P21= cross-correlation plane in fourier domain
%f1= fft of window1/image1
%f2= fft of window2/image2
%Sx= window size Y
%Sy= window size X
%D= Approx Diameter of particle image (e.g. [2.8 2.8]), this is used to
%initialize the autocorrelation diameter estimation using subpixel fit

%cnorm= Matrix of ones of size Sx,Sy this is non unity if you want to
%correct for spatial winfow filtering

% fftindx,fftindy= fftshift indices I guess a simple fftshift can be used
% instead, but just to be consistent with prana functions

%G= Cross correlation plane of two windows

%region1, region2= spatially windowed image

%DXtemp,DYtemp= Cross correlation peak diameter estimated from 3pt subpixel
%fit on cross-correlation plane G

%MIest = If MI is already estimated then use that otherwise calculate MI

%Output
%Ixx=PDF diameter in X direction
%Iyy=PDF diameter in X direction
%biasx=bias uncertainty in X direction
%biasy=bias uncertainty in Y direction
%Neff=Effective number of particles
%Autod= Average Autocorrelation diameter


if MIest==-1
% If Mi has not been estimated
%Autocorrelations
P11 = f1.*conj(f1);
P22 = f2.*conj(f2);
Auto1 = ifftn(P11,'symmetric');
Auto2 = ifftn(P22,'symmetric');
Auto1 = Auto1(fftindy,fftindx);
Auto2 = Auto2(fftindy,fftindx);
Auto1=abs(Auto1);
Auto2=abs(Auto2);
nAuto1 = Auto1-min(Auto1(:)); % Autocorrelation plane of image 1
nAuto2 = Auto2-min(Auto2(:)); % Autocorrelation plane of image 2


% 3 pt Gaussian fit to Autocorrelation Diameter
[~,~,~,~,Dauto1x3,Dauto1y3,~]=subpixel(nAuto1,Sx,Sy,cnorm,1,0,D);
[~,~,~,~,Dauto2x3,Dauto2y3,~]=subpixel(nAuto2,Sx,Sy,cnorm,1,0,D);
Diap1=sqrt(Dauto1x3*Dauto1y3/2);
Diap2=sqrt(Dauto2x3*Dauto2y3/2);

%Average Autocorrelation Diameter
Autod=mean([Diap1 Diap2]);


%MI Calculation
INTS1 = max(region1(:));
INTS2 = max(region2(:));
[MI,~,~,~,~,~] = MI_Cal_SCC(G,nAuto1,nAuto2,INTS1,INTS2,Dauto1x3,Dauto1y3,Dauto2x3,Dauto2y3,Sx,Sy,fftindx,fftindy);
else
% Use estimated MI
MI=MIest;
Autod=0;
end
%Cross-correlation subpixel fit
% [U,V,~,~,DXtemp,DYtemp,~]=subpixel(G,Sx,Sy,cnorm,1,0,D);


if isnan(DXtemp)
DXtemp=sqrt(2)*2.8;
end
if isnan(DYtemp)
DYtemp=sqrt(2)*2.8;
end

% Estimate the diameter of the cross-correlation peak (with initiation of
% 3pt fit estimates of correlation diameter)
[~,~,~,~,dxc1,dyc1,alpha2]=subpixel(G,Sx,Sy,cnorm,3,0,(1/sqrt(2))*[DXtemp DYtemp]);
% Taking care of peak rotation
DCCx = sqrt( (cos(alpha2)^2*dxc1^2 + sin(alpha2)^2*dyc1^2) );
DCCy = sqrt( (sin(alpha2)^2*dxc1^2 + cos(alpha2)^2*dyc1^2) );
% If estimates are too big than the 3pt fit estimate then default to 3 pt
% fit estimate of the correlation diameter
if DCCx >sqrt(2)*DXtemp
DCCx=DXtemp;
end
if DCCy >sqrt(2)*DYtemp
DCCy=DYtemp;
end

%Use Cross-correlation diameter estimate to define convolving Gaussian Diameter
Dconv=(1/sqrt(2))*[DCCx DCCy];

% Finding the Phase correlation
W = ones(Sy,Sx);
Wden = sqrt(P21.*conj(P21));
W(Wden~=0) = Wden(Wden~=0);
R = P21./W; % This is effectively the fourier transform of the PDF


% constructing the gaussian which will be convolved with the pdf
[Xt,Yt]=meshgrid(1:Sx,1:Sy);
gfilt=exp(-(4/Dconv(1)^2).*(Xt-Sx/2-1).^2-(4/Dconv(2)^2).*(Yt-Sy/2-1).^2);
Gfilt=abs(fftn(gfilt,[Sy Sx]));

%Convolving the PDF and the Gaussian
G1 = ifftn(R.*Gfilt,'symmetric');
G1 = G1(fftindy,fftindx);
G1 = abs(G1);
G1=G1-min(G1(:));


%subpixel estimation using least squres guassfit for the convolved plane G1
[gpx,gpy,~,~,dx1,dy1,alpha1]=subpixel(G1,Sx,Sy,cnorm,3,0,Dconv);

%find the PDF major and minor axis
Px=real(((dx1(1)^2 - 2*(Dconv(1))^2)).^0.5);
Py=real(((dy1(1)^2 - 2*(Dconv(2))^2)).^0.5);

%If the pdf diameter comes out to be zero or imaginary try 3point fit for
%the convolved plane G1
if Px==0 || Py==0
% if zero try 3point fit
[gpx,gpy,~,~,dx1,dy1,~]=subpixel(G1,Sx,Sy,cnorm,1,0,Dconv);

Px=real(((dx1(1)^2 - 2*(Dconv(1))^2)).^0.5);
Py=real(((dy1(1)^2 - 2*(Dconv(2))^2)).^0.5);
alpha1=0;
%project major and minor axis to X and Y axes
Ixx = sqrt( 1/16 * (cos(alpha1)^2*Px^2 + sin(alpha1)^2*Py^2) );
Iyy = sqrt( 1/16 * (sin(alpha1)^2*Px^2 + cos(alpha1)^2*Py^2) );


else
% If not zero then continue
%project to axes
Ixx = sqrt( 1/16 * (cos(alpha1)^2*Px^2 + sin(alpha1)^2*Py^2) );
Iyy = sqrt( 1/16 * (sin(alpha1)^2*Px^2 + cos(alpha1)^2*Py^2) );

end

% The mean particle diameter is estimated from the mean cross-correlation
% peak diameter divided by sqrt(2)
part_dia=(1/sqrt(2))*mean([DCCx DCCy]);

% Effective number of pixels is MI times a circular blob of pixels with
% mean particle diameter
Neff=MI*((pi/4)*(part_dia)^2);

%Bias: This is the peak location of the convolved gaussian plane, typically
%this is done for multipass converged correlation plane in which case the
%peak should be at zero and any deviation is the bias. However this will
%not work for first pass.
biasx=gpx;
biasy=gpy;

% The gradient correction and scaling is done outside PIVwindowed as the
% gradient estimation requires the full velcoity field

% %Gradient correction using velocity gradient field Udiff and Vdiff
% Ixxt= real(sqrt(Ixx.^2 - (Autod.^2/16).*(Udiff).^2 ));
% Iyyt= real(sqrt(Iyy.^2 - (Autod.^2/16).*(Vdiff).^2 ));
%
%
% %MC uncertainty after scaling and bias correction
% MCx=sqrt(biasx^2+(Ixxt^2)/Neff);
% MCy=sqrt(biasy^2+(Iyyt^2)/Neff);
end

% JJC: this function is an exact duplicate of the external function.
% Is it necessary?
function [MI,Nim1,Nim2,INTS,Diapx,Diapy] = MI_Cal_SCC(G,nAuto1,nAuto2,INTS1,INTS2,Diap1x,Diap1y,Diap2x,Diap2y,Sx,Sy,fftindx,fftindy)
%[MI,INTS,DiaP] = MI_Cal_SCC(G,nAuto1,nAuto2,INTS1,INTS2,Sx,Sy,fftindx,fftindy)
%[MI,INTS,DiaP] = MI_Cal_SCC( region1,region2,Sx,Sy,fftindx,fftindy)
%This function is used to calculate the mutual information between two
%consecutive frames using SCC method
% region 1 and 2 are the particle image within the interrogation window.
% Sx and Sy are the correlation window size
% fftindx and fftindy are fftshift indicies
% We will first find the maximum intensity from the image, and average
% particle diameter from particle image autocorrelation. A standard
% gaussian particle is built based on these two parameter. Then the
% contritbution of one particle is caluclate by take the auotcorrelatio
% peak of stardard gaussian particle. The primary peak on correlation
% plane is a summation of the contribution of all correlated particles.
% MI is the ratio between contribution of all correlated particles and
% one particle.



% Diapx=(1/sqrt(2))*mean([Diap1x Diap2x]);
% Diapy=(1/sqrt(2))*mean([Diap1y Diap2y]);
Diapx=(1/sqrt(2))*(Diap1x*Diap2x)^0.5;
Diapy=(1/sqrt(2))*(Diap1y*Diap2y)^0.5;

%make analytical gaussian particle and calculate
%autocorrelaiton of the standard particle
xco = 1:Sx;xco = repmat(xco,[Sx,1]); % build X axis
yco = 1:Sy;yco = repmat(yco',[1,Sy]); % build Y axis
% INTS = (INTS1+INTS2)/2; % intensity of standard Gaussian particle is the mean (maximum) intensity of two frames
INTS = sqrt(INTS1*INTS2); % intensity of standard Gaussian particle is the mean (maximum) intensity of two frames

% % DiaP = (DiaP1+DiaP2)/2; % diameter of standard Gaussian particle is the mean diameter of two frames
% % fg = INTS*exp(-8*((xco-round(Sx/2)).^2+(yco-round(Sy/2)).^2)/DiaP^2); % build the particle intensity distribution based on 2D Gaussian distribution

fg = INTS*exp(-8*((xco-round(Sx/2)).^2/(Diapx^2)+(yco-round(Sy/2)).^2/(Diapy^2))); % build the particle intensity distribution based on 2D Gaussian distribution

fg = fftn(fg,[Sy,Sx]); %FFT
Pg = fg.*conj(fg); %FFT based correlation
Sp = ifftn(Pg,'symmetric'); % convert to time domain
Sp = Sp(fftindy,fftindx); % Sp is the autocorrelation of the standadrd Gaussian particle

%use cross correaltion plane to get MI
Gnorm = G-min(G(:)); %minimum correlation subtraction to elminate background noise effect
[~,Gind] = max(Gnorm(:)); % find the primary peak location
shift_locyg = 1+mod(Gind-1,Sy); % find the Y coordinate of the peak
shift_locxg = ceil(Gind/Sy); % find the X coordinate of the peak
GXshift = Sy/2+1-shift_locxg; % find the distance between the peak location to the center of correlation plane in X direction
GYshift = Sx/2+1-shift_locyg; % find the distance between the peak location to the center of correlation plane in Y direction
SGnorm = circshift(Gnorm,[GYshift,GXshift]); % shift the peak to the center of the plane
%keyboard;
MI = max(SGnorm(:))/max(Sp(:)); % MI is the ratio between the contribution of all correlated particle and the contribution of one particle
Nim1=max(nAuto1(:))/max(Sp(:));
Nim2=max(nAuto2(:))/max(Sp(:));



end

0 comments on commit d952b96

Please sign in to comment.
You can’t perform that action at this time.