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% GET_INTEGRATION_MATRIX Generate sparse integration matrix that sums up
% values according to the provided integration mask
%
%
% int_matrix = get_integration_matrix(mask)
%
% Inputs:
% **mask - 2D integer array, 0 = ignored regions, 1:max(mask) are different sectors that will be summed separatelly
% Outputs:
% ++int_matrix - 2D sparse matrix
%
%
%%%%%%%%%%%%%%%%%%%%% HOW TO USE %%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% % create some data
% img = single(imread('cameraman.tif'));
% img = repmat(img, 1,1,10); % just add there 3rd dimension
% Np = size(img);
%
% %% define parameters of the integration matrix
% Nrad = 20;
% Nsec = 30;
% center_pos = Np/2-30;
%
%
% %% generate radial and sector masks, needs to be modified if center != Np/2
% [mask, radial_mask, sector_mask] = get_radial_integration_mask(Np, Nrad, Nsec, center_pos);
%
% %% check the generated sector mask
% figure(1)
% imagesc(mask); axis off image
% title('Radial & Angular sectors')
% colormap(hsv)
% drawnow
%
% % generate the integration 2D sparse matrix
% T = get_integration_matrix(mask);
%
%
% %% perform sparse matrix based integration
% tic
% img_sum = single(reshape((T*reshape(double(img), prod(Np(1:2)), [])), Nrad,Nsec, []));
% toc
%
% %% perform matlab based integration for comparison
% tic
% img_sum_0 = zeros(Nrad,Nsec, size(img,3));
% for nz = 1:size(img,3)
% im = img(:,:,nz);
% for i = 1:Nrad
% m = radial_mask == i;
% for j = 1:Nsec
% img_sum_0(i,j,nz) = sum(im( m & sector_mask == j ));
% end
% end
% end
% toc
%
%
% % show the first frame to check that the methods are identical
% figure
% subplot(1,2,1)
% imagesc(img_sum_0(:,:,1)); axis image
% title('Matlab')
% subplot(1,2,2)
% imagesc(img_sum(:,:,1)); axis image
% title('Sparse matrix')
%*-----------------------------------------------------------------------*
%| |
%| Except where otherwise noted, this work is licensed under a |
%| Creative Commons Attribution-NonCommercial-ShareAlike 4.0 |
%| International (CC BY-NC-SA 4.0) license. |
%| |
%| Copyright (c) 2017 by Paul Scherrer Institute (http://www.psi.ch) |
%| |
%| Author: CXS group, PSI |
%*-----------------------------------------------------------------------*
% You may use this code with the following provisions:
%
% If the code is fully or partially redistributed, or rewritten in another
% computing language this notice should be included in the redistribution.
%
% If this code, or subfunctions or parts of it, is used for research in a
% publication or if it is fully or partially rewritten for another
% computing language the authors and institution should be acknowledged
% in written form in the publication: “Data processing was carried out
% using the “cSAXS matlab package” developed by the CXS group,
% Paul Scherrer Institut, Switzerland.”
% Variations on the latter text can be incorporated upon discussion with
% the CXS group if needed to more specifically reflect the use of the package
% for the published work.
%
% A publication that focuses on describing features, or parameters, that
% are already existing in the code should be first discussed with the
% authors.
%
% This code and subroutines are part of a continuous development, they
% are provided “as they are” without guarantees or liability on part
% of PSI or the authors. It is the user responsibility to ensure its
% proper use and the correctness of the results.
function int_matrix = get_integration_matrix(mask)
N = max(mask(:));
Np = size(mask);
int_matrix = zeros(prod(Np),2);
ind_start = 1;
for id = 1 : max(mask(:))
[i,j] = find(mask == id);
ind_end = ind_start + length(i)-1;
int_matrix(ind_start:ind_end,:) = [id*ones(length(i),1),i+(j-1)*Np(1)];
ind_start = ind_end + 1;
end
% convert to sparse matrix
int_matrix = sparse(int_matrix(1:ind_end,1),int_matrix(1:ind_end,2),ones(ind_end,1), N, prod(Np));
end