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Added functionality for automatic download of filters
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@fordanic
fordanic committed Feb 4, 2015
1 parent 2efd8ec commit 0a94c66
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Showing 2 changed files with 65 additions and 37 deletions.
26 changes: 26 additions & 0 deletions compute_structure_tensor2d.m
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Original file line number Diff line number Diff line change
Expand Up @@ -64,6 +64,19 @@

if strcmp(mode,'quadrature')
% Load filters
if ~exist('quadratureFiltersForStructureTensor2D.mat','file')
disp('Quadrature filters needed for compute_structure_tensor2d are not available.')
answer = input('Would you like to download these filters? [y]/[n] ','s');
if strcmpi(answer,'y') || strcmpi(answer,'yes')
folder = fileparts(mfilename('fullpath'));
currentFolder = pwd;
cd(folder)
urlwrite(...
'https://github.com/fordanic/tensor-processing/blob/master/quadratureFiltersForStructureTensor2D.mat',...
'quadratureFiltersForStructureTensor2D.mat')
cd(currentFolder)
end
end
load quadratureFiltersForStructureTensor2D

% Select filters
Expand All @@ -83,6 +96,19 @@
end
elseif strcmp(mode,'monomials')
% Load filters
if ~exist('monomialsForStructureTensor2D.mat','file')
disp('Monomial filters needed for compute_structure_tensor2d are not available.')
answer = input('Would you like to download these filters? [y]/[n] ','s');
if strcmpi(answer,'y') || strcmpi(answer,'yes')
folder = fileparts(mfilename('fullpath'));
currentFolder = pwd;
cd(folder)
urlwrite(...
'https://github.com/fordanic/tensor-processing/blob/master/monomialsForStructureTensor2D.mat',...
'monomialsForStructureTensor2D.mat')
cd(currentFolder)
end
end
load monomialsForStructureTensor2D

% Select filters
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76 changes: 39 additions & 37 deletions compute_structure_tensor3d.m
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@
% domain (1.0)
% 'normalize' - Set to true to normalize the estimated tensor (false)
% 'mode' - 'quadrature'/'monomials'
%
% OUTPUT ARGUMENTS
% t11 - Tensor element 1,1
% t12 - Tensor element 1,2
Expand Down Expand Up @@ -59,8 +60,6 @@
eval([varargin{k},'=varargin{',int2str(k+1),'};']);
end;

global USE_CUDA;

% Initialize tensor elements
t11 = zeros(size(input));
t12 = zeros(size(input));
Expand All @@ -71,6 +70,19 @@

if strcmp(mode,'quadrature')
% Load filters
if ~exist('quadratureFiltersForStructureTensor3D.mat','file')
disp('Quadrature filters needed for compute_structure_tensor3d are not available.')
answer = input('Would you like to download these filters? [y]/[n] ','s');
if strcmpi(answer,'y') || strcmpi(answer,'yes')
folder = fileparts(mfilename('fullpath'));
currentFolder = pwd;
cd(folder)
urlwrite(...
'https://github.com/fordanic/tensor-processing/blob/master/quadratureFiltersForStructureTensor3D.mat',...
'quadratureFiltersForStructureTensor3D.mat')
cd(currentFolder)
end
end
load quadratureFiltersForStructureTensor3D

% Select filters
Expand All @@ -83,12 +95,7 @@
eval(['m33 = ',scale,'.m33;']);

for k = 1 : 6
if ~isempty(USE_CUDA) && USE_CUDA
q = CUDA_convolution3d(input,real(qFilt{k}),'edge_extraction');
q = q + 1i*CUDA_convolution3d(input,imag(qFilt{k}),'edge_extraction');
else
q = imfilter(input,qFilt{k},'conv','same','replicate');
end
q = imfilter(input,qFilt{k},'conv','same','replicate');

% Estimate T
Aq = abs(q);
Expand All @@ -101,26 +108,30 @@
end
elseif strcmp(mode,'monomials')
% Load filters
if ~exist('monomialsForStructureTensor3D.mat','file')
disp('Monomial filters needed for compute_structure_tensor3d are not available.')
answer = input('Would you like to download these filters? [y]/[n] ','s');
if strcmpi(answer,'y') || strcmpi(answer,'yes')
folder = fileparts(mfilename('fullpath'));
currentFolder = pwd;
cd(folder)
urlwrite(...
'https://github.com/fordanic/tensor-processing/blob/master/monomialsForStructureTensor3D.mat',...
'monomialsForStructureTensor3D.mat')
cd(currentFolder)
end
end
load monomialsForStructureTensor3D

% Select filters
eval(['mFilt1 = ',scale,'.f1;']);
eval(['mFilt2 = ',scale,'.f2;']);

if ~isempty(USE_CUDA) && USE_CUDA
for k = 1 : length(f1)
q1{k} = CUDA_convolution3d(input,mFilt1{k},'edge_extraction');
end
for k = 1 : length(f2)
q2{k} = CUDA_convolution3d(input,mFilt2{k},'edge_extraction');
end
else
for k = 1 : length(f1)
q1{k} = imfilter(input,mFilt1{k},'conv','same','replicate');
end
for k = 1 : length(f2)
q2{k} = imfilter(input,mFilt2{k},'conv','same','replicate');
end
for k = 1 : length(f1)
q1{k} = imfilter(input,mFilt1{k},'conv','same','replicate');
end
for k = 1 : length(f2)
q2{k} = imfilter(input,mFilt2{k},'conv','same','replicate');
end

t11 = q1{1}.*q1{1};
Expand Down Expand Up @@ -162,21 +173,12 @@
if average
Tcert = sqrt(t11.^2 + 2*t12.^2 + 2*t13.^2 + t22.^2 + 2*t23.^2 + t33.^2);

if ~isempty(USE_CUDA) && USE_CUDA
t11 = CUDA_normalized_averaging3d_separable(t11, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
t12 = CUDA_normalized_averaging3d_separable(t12, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
t13 = CUDA_normalized_averaging3d_separable(t13, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
t22 = CUDA_normalized_averaging3d_separable(t22, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
t23 = CUDA_normalized_averaging3d_separable(t23, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
t33 = CUDA_normalized_averaging3d_separable(t33, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
else
t11 = averaging3d(t11, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
t12 = averaging3d(t12, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
t13 = averaging3d(t13, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
t22 = averaging3d(t22, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
t23 = averaging3d(t23, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
t33 = averaging3d(t33, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
end
t11 = averaging3d(t11, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
t12 = averaging3d(t12, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
t13 = averaging3d(t13, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
t22 = averaging3d(t22, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
t23 = averaging3d(t23, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
t33 = averaging3d(t33, Tcert, sizeAveragingFilter, sigmaAveragingFilter);
end

if normalize
Expand Down

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