/
ncorr_alg_convertanalysis.m
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/
ncorr_alg_convertanalysis.m
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function [plots_disp_new,rois_new,outstate] = ncorr_alg_convertanalysis(img_new,imgs_old,plots_u_old,plots_v_old,rois_old,spacing,num_img,total_imgs)
% This function is used to convert displacements from the old configuration
% to the new configuration. Can be Lagrangian to Eulerian or vice-versa,
% which is why "old" and "new" are used instead of Lagrangian and Eulerian.
%
% Inputs -----------------------------------------------------------------%
% img_new - struct; Contains struct('imginfo',{},'roi',{}) in the new
% configuration.
% imgs_old - struct; Contains struct('imginfo',{},'roi',{}) in the old
% configuration.
% plots_u_old - cell; contains "old" u displacement fields
% plots_v_old - cell; contains "old" v displacement fields
% rois_old - ncorr_class_roi; contains ROIs corresponding to the
% u and v plots. Note these are reduced by default.
% spacing - integer; spacing parameter
% num_img - integer; reference image number
% total_imgs - integer; total number of images
%
% Outputs ----------------------------------------------------------------%
% plots_disp_new - struct; contains struct('plot_u_new',{},'plot_v_new',{})
% rois_new - struct; contains ROIs corresponding to the "new" u and v
% displacement plots
% outstate - integer; returns either out.cancelled, out.failed, or
% out.success.
%
% Returns failed if seed placement fails.
% Initialize outputs
outstate = out.cancelled;
plots_disp_new = struct('plot_u_new',{},'plot_v_new',{});
rois_new = ncorr_class_roi.empty;
% Extrapolate displacement plots to improve interpolation near the
% boundary points.
% border_interp is the border added to the displacements when they are
% extrapolated.
border_interp = 20; % MUST BE GREATER THAN OR EQUAL TO 2
plots_u_interp_old = cell(0);
plots_v_interp_old = cell(0);
% Note that ncorr_alg_extrapdata will return a separate extrapolated
% array for each region within a ROI. This is done to prevent
% displacements from adjacent regions from influencing each other.
% Also note that the ordering returned forms a correspondence between
% the regions stored in rois_old.
for i = 0:length(imgs_old)-1
plots_u_interp_old{i+1} = ncorr_alg_extrapdata(plots_u_old{i+1},rois_old(i+1).formatted(),int32(border_interp));
plots_v_interp_old{i+1} = ncorr_alg_extrapdata(plots_v_old{i+1},rois_old(i+1).formatted(),int32(border_interp));
end
% Convert displacement plots to B-spline coefficients; these are used
% for interpolation. Make sure to do this for each region.
for i = 0:length(imgs_old)-1
for j = 0:length(rois_old(i+1).region)-1
plots_u_interp_old{i+1}{j+1} = ncorr_class_img.form_bcoef(plots_u_interp_old{i+1}{j+1});
plots_v_interp_old{i+1}{j+1} = ncorr_class_img.form_bcoef(plots_v_interp_old{i+1}{j+1});
end
end
% Set Seeds ----------------------------------------------------------%
% This will automatically try to seed as many regions as possible.
roi_new_reduced = img_new.roi.reduce(spacing);
convertseedinfo = cell(0);
seedwindow = 1;
for i = 0:length(imgs_old)-1
% Will return either success or failed. Returns success if at
% least one region is seeded.
[convertseedinfo{i+1},outstate_convertseeds] = ncorr_alg_convertseeds(plots_u_old{i+1}, ...
plots_v_old{i+1}, ...
plots_u_interp_old{i+1}, ...
plots_v_interp_old{i+1}, ...
rois_old(i+1), ...
roi_new_reduced, ...
seedwindow, ...
spacing, ...
border_interp);
% Must check to make sure seedinfo isnt empty
if (outstate_convertseeds ~= out.success)
% For now just fail the whole analysis since this will result
% in an empty plot.
h_error = errordlg('Seeding failed for the conversion analysis. Please rerun DIC analysis and make sure regions have a large contiguous region.','Error','modal');
uiwait(h_error);
outstate = out.failed;
break;
end
end
if (outstate ~= out.failed)
% Find displacements in new configuration ------------------------%
% Format seeds
convertseedinfo_f = convertseedinfo;
for i = 0:length(convertseedinfo)-1
for j = 0:length(convertseedinfo{i+1})-1
convertseedinfo_f{i+1}(j+1).num_region_new = int32(convertseedinfo{i+1}(j+1).num_region_new);
convertseedinfo_f{i+1}(j+1).num_region_old = int32(convertseedinfo{i+1}(j+1).num_region_old);
end
end
% Initialize structs for output
plots_disp_new_prelim = struct('plot_u_new',{},'plot_v_new',{});
rois_new_prelim = ncorr_class_roi.empty;
for i = 0:length(imgs_old)-1
% ncorr_alg_convert returns either success or cancelled.
[plot_disp_new_buffer,outstate_convert] = ncorr_alg_convert(plots_u_interp_old{i+1}, ...
plots_v_interp_old{i+1}, ...
rois_old(i+1).formatted(), ...
roi_new_reduced.formatted(), ...
convertseedinfo_f{i+1}, ...
int32(spacing), ...
int32(border_interp), ...
int32(num_img+i), ...
int32(total_imgs));
% Check if analysis was cancelled
if (outstate_convert ~= out.success)
return;
end
% Store new displacement plots and then get the union of valid
% points with roi_new_reduced.
plots_disp_new_prelim(i+1).plot_u_new = plot_disp_new_buffer.plot_u_new;
plots_disp_new_prelim(i+1).plot_v_new = plot_disp_new_buffer.plot_v_new;
rois_new_prelim(i+1) = roi_new_reduced.get_union(plot_disp_new_buffer.plot_validpoints,0);
end
% Set outputs
for i = 0:length(plots_disp_new_prelim)-1
plots_disp_new(i+1) = plots_disp_new_prelim(i+1);
rois_new(i+1) = rois_new_prelim(i+1);
end
outstate = out.success;
end
end