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94 lines
4.0 KiB
Matlab
94 lines
4.0 KiB
Matlab
% get_mask - estimate support mask for the provided reconstructed volume
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%
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% [mask, W_rec] = get_mask(rec_0, mask_threshold, mask_dilate, show_mask)
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%
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% Inputs:
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% **rec_0 reconstruction volume
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% **mask_threshold relative threshold with respect to the maximum
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% **mask_dilate mask dilatation in pixels
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% **show_mask true / false if you want to plot the mask
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% Outputs:
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% ++mask binary mask
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% ++W_rec importance weights for the reconstructioin
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%*-----------------------------------------------------------------------*
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%| |
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%| Except where otherwise noted, this work is licensed under a |
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%| Creative Commons Attribution-NonCommercial-ShareAlike 4.0 |
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%| International (CC BY-NC-SA 4.0) license. |
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%| |
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%| Copyright (c) 2018 by Paul Scherrer Institute (http://www.psi.ch) |
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%| |
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%| Author: CXS group, PSI |
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%*-----------------------------------------------------------------------*
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% You may use this code with the following provisions:
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%
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% If the code is fully or partially redistributed, or rewritten in another
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% computing language this notice should be included in the redistribution.
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%
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% If this code, or subfunctions or parts of it, is used for research in a
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% publication or if it is fully or partially rewritten for another
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% computing language the authors and institution should be acknowledged
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% in written form in the publication: “Data processing was carried out
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% using the “cSAXS matlab package” developed by the CXS group,
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% Paul Scherrer Institut, Switzerland.”
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% Variations on the latter text can be incorporated upon discussion with
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% the CXS group if needed to more specifically reflect the use of the package
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% for the published work.
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%
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% A publication that focuses on describing features, or parameters, that
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% are already existing in the code should be first discussed with the
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% authors.
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%
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% This code and subroutines are part of a continuous development, they
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% are provided “as they are” without guarantees or liability on part
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% of PSI or the authors. It is the user responsibility to ensure its
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% proper use and the correctness of the results.
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function [mask, W_rec] = get_mask(rec_0, mask_threshold, mask_dilate, show_mask)
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if nargin < 4
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show_mask = false;
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end
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%% get mask
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Nlayers = size(rec_0,3);
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Npix = size(rec_0,1);
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mask_dilate = ceil(mask_dilate);
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mask = rec_0 > mask_threshold * quantile(rec_0(:), 0.99);
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mask = convn(single(mask), ones(mask_dilate,mask_dilate,mask_dilate, 'single'), 'same') > 1e-3*mask_dilate^3;
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%% get importance weighting for difference regions
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% importance weighting
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W_rec = gpuArray(single(tukeywin(Npix, 0.2) .* tukeywin(Npix, 0.2)' .* reshape(tukeywin(Nlayers, 0.2)',1,1,[]) )); % avoid edge issues
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W_rec = W_rec .* mask;
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W_rec = utils.imgaussfilt3_fft(W_rec,mask_dilate/2);
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W_rec = gather(W_rec);
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if show_mask
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plotting.smart_figure(212)
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plotting.imagesc_tomo(mask)
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suptitle('Estimated mask')
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drawnow
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%% show mask
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plotting.smart_figure(46)
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subplot(1,2,1)
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plotting.imagesc3D(rec_0.* ~mask, 'init_frame', Nlayers/2)
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colorbar
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axis off image
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colormap bone
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title('Example of residuum after applied mask')
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subplot(1,2,2)
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hist(rec_0(1:100:end), 100)
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axis tight
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drawnow
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end
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end
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