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R6289/code/poststeps_to_compute_motion.m
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poststeps_to_compute_motion.m
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function w = poststeps_to_compute_motion(I1,I2,I1Match,param,t,tmpdir,lambdas,gammas)
%% Description
% Compute motion with a brigthness constancy data term defined on (I1,I2)
% and a feature matching similarity term defined on (I1Match,I2Match). The
% feature matches are computed by the DeepMatching method [Revaud et al. 2016], with the
% source code provided by the authors
% For more details see the paper: "Imaging neural activity in the ventral
% nerve cord of behaving adult Drosophila", bioRxiv
%
% [Revaud et al. 2016] J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid (2016) "DeepMatching:
% Hierarchical Deformable Dense Matching. Int J Comput Vis 120:300–323
%% Input
% I1,I2: Images used for the brightness constancy term
% I1Match,I2Match: Images used by the feature matching similarity term
% fnDeepMatching: filename of the deepmatching code
% param: parameters of the algorithm (see 'default_parameters.m')
% t: number of the frame in the image sequence
%
% Copyright (C) 2017 D. Fortun, denis.fortun@epfl.ch
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
sz0 = size(I1);
I1=padarray(I1,[15,15],'replicate');
I2=padarray(I2,[15,15],'replicate');
fnI1= [tmpdir,'/I1_',num2str(t),'.png'];
fnI2= [tmpdir,'/I2_',num2str(t),'.png'];
fnMatch=[tmpdir,'/match_',num2str(t),'.txt'];
formatSpec = '%u %u %u %u %f %u';
sizeCorresp = [6 Inf];
f=fopen(fnMatch);
while f==-1
f=fopen(fnMatch);
end
corresp = fscanf(f,formatSpec,sizeCorresp);
while size(corresp,1)==0
corresp = fscanf(f,formatSpec,sizeCorresp);
end
command = ['rm ',fnI1]; s = system(command);
command = ['rm ',fnI2]; s = system(command);
command = ['rm ',fnMatch]; s = system(command);
thresh=param.threshMatch;
Iseg=segment(I1Match,'variance',thresh);
matches = zeros(5,1);
k=0;
for i=1:size(corresp,2)
if corresp(1,i)>0 && corresp(1,i)<=sz0(2) && corresp(2,i)>0 && corresp(2,i)<=sz0(1)
if Iseg(corresp(2,i),corresp(1,i)) == 1
k = k+1;
matches(1,k) = corresp(1,i);
matches(2,k) = corresp(2,i);
matches(4,k) = corresp(4,i)-corresp(2,i);
matches(3,k) = corresp(3,i)-corresp(1,i);
end
end
end
scores = corresp(5,:);
idx = find(corresp(5,:));
scores = scores(scores~=0);
scores = (scores-min(scores))/(max(scores)-min(scores));
for j=1:length(idx)
matches(5,idx(j)) = scores(j);
end
%% Optical flow
print = 0; % if print =1:verbose mode
disp = 0; % if disp=1; displays the results at each iteration
tmp_param = param;
idx=1;
w = zeros(sz0(1),sz0(2),2,length(lambdas)*length(gammas));
for l = lambdas
for g = gammas
tmp_param.lambda = l;
tmp_param.gamma = g;
tmp_w = of_l1_l2_fm_admm(I1,I2,sz0,matches,tmp_param,print,disp);
w(:,:,:,idx) = crop_fit_size_center(tmp_w,[sz0(1),sz0(2),2]);
idx = idx+1;
end
end
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