Optic_flow_RF_estimation_Zhang_et_al_2022/code/CaPreprocessing/RegistrationSegmentation/applyROImask.m

function ROItrace = applyROImask(imdata,roimask,std_old_im)
% Input:
%   imdata: M-by-N-by-F double matrix, Ca recording movie
%   roimask: M-by-N double integer matrix of the imported ROI mask
%   std_old_im(optional): M-by-N double matrix, the standard deviation
%   image of the Ca recording data used for creating the ROI mask imported
% Output:
%   ROItrace: 1-by-K cells, each cell contains a 1-by-F array for the Ca
%   trace extracted from the input imdata;
if exist('std_old_im','var')
    std_new_im = std(imdata,1,3);
    tformEstimate = imregcorr(std_old_im,std_new_im,'transformtype','translation');
    Rfixed = imref2d(size(std_old_im));
    roiInd = unique(roimask(:));
    roimask = imwarp(roimask,tformEstimate,'OutputView',Rfixed,'Interp','nearest');
    figure(1)
    imshowpair(imwarp(std_old_im,tformEstimate,'OutputView',Rfixed),std_new_im)
else
    roiInd = unique(roimask(:));
end
numROI = length(roiInd)-1; % because 1 in roimask means background
ROItrace = cell(1,numROI);
[~,~,nf] = size(imdata);
imdata = reshape(imdata,[],nf);
for i = 2:numROI+1
    ROItrace{i-1} = squeeze(sum(imdata(roimask == roiInd(i),:)));
end
end