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Commit 00b867fe authored by jml1g18's avatar jml1g18
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added beta support for time-series PIV

parent 5c9b410c
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image_preprocess.m
View file @ 00b867fe
......@@ -70,6 +70,9 @@ function im_filt = image_preprocess(im, filters)
if isfield(filter, 'n'), n = filter.n; end
thresh = im_med + n*im_std;
im_filt = min(im_filt, thresh);
elseif strcmp(filter.type, 'null')
%% null filter, do nothing
im_filt = im_filt;
else
error('unrecognised filter type %s', filter.type);
end
......
job_create.m
View file @ 00b867fe
......@@ -9,31 +9,35 @@ addpath(genpath(fileparts(mfilename('fullpath'))));
%% input parameters: file paths
% where to save jobs
%job_idx = 1;
job_dir = 'H:\davis\multiscale\Gen1_12\jobs\';
job_idx = 1;
job_dir = 'J:\FEE\EFMRL\John\TR-IM7\jobs\';
job_pat = '%s/job%03d.mat';
% timeseries PIV flag
% in timeseries PIV, correlations are conducted between subsequent images
% in a timeseries
b_timeseries = true;
% path of images to process
%src_dir = 'H:\davis\multiscale\Gen1_12\U10\U10_x-axis=257.988_x-axis=257.987_x-axis=229.986\';
src_dir = 'J:\FEE\EFMRL\John\TR-IM7';
src_pat = '%s/B%05d.im7'; % source image to process
src_range = 1:1000; % range of source images to process
src_range = 1:3; % range of source images to process
% background & mask
bg_dir = src_dir; % background image directory
bg_pat = '%s/minmax.mat'; % background image (ignore if not using)
msk_dir = [src_dir '/MASK/']; % mask image directory
msk_pat = '%s/B0001.im7'; % pattern for mask image input (ignore if not using)
msk_pat = '%s/B0002.im7'; % pattern for mask image input (ignore if not using)
% where to save result
%vec_dir = [src_dir '/C12']; % directory to save vector fields to
vec_dir = [src_dir '/C12']; % directory to save vector fields to
vec_pat = '%s/B%05d.mat'; % output vector field file pattern
%% input parameters: processing
% processing flags
b_use_mask = true; % use mask file?
b_use_mask = false; % use mask file?
b_overwrite = true; % overwrite existing vector fields?
b_execute_now = false; % start processing now?
b_execute_now = true; % start processing now?
% PIV correlation settings
i_frames = {[1 2],[3 4]}; % indices of frame pairs to cross-correlate
i_frames = {[1 1],[2 2], [3 3]}; % indices of frame pairs to cross-correlate
n_passes = 3; % number of passes
n_peaks = 3; % number of correlation peaks to detect
wsize = [64 64; 32 32; 32 32];% window size
......@@ -52,27 +56,27 @@ min_filt_size = [17 17]; % sliding bg filter size
gauss_filt_size = [3 3]; % gaussian filter size
norm_filt_size = [17 17]; % normalisation filter size
filters = {};
filters = {struct('type', 'null')};
% subtract background image: will require background image to be supplied
filters{end+1} = struct('type', 'ssbg');
% subtract sliding minimum
%filters{end+1} = struct('type', 'ssmin', 'size', min_filt_size);
% max-clip bright particles at threshold = median(im) + n*sigma(im)
filters{end+1} = struct('type', 'clip', 'n', 2);
% 2D Gaussian filter
filters{end+1} = struct('type', 'gaussian', 'size', gauss_filt_size, 'sigma', 0.67);
% min-max normalisation filter
filters{end+1} = struct('type', 'norm', 'size', norm_filt_size, 'max_gain', 1/32);
% % subtract background image: will require background image to be supplied
% filters{end+1} = struct('type', 'ssbg');
% % subtract sliding minimum
% %filters{end+1} = struct('type', 'ssmin', 'size', min_filt_size);
% % max-clip bright particles at threshold = median(im) + n*sigma(im)
% filters{end+1} = struct('type', 'clip', 'n', 2);
% % 2D Gaussian filter
% filters{end+1} = struct('type', 'gaussian', 'size', gauss_filt_size, 'sigma', 0.67);
% % min-max normalisation filter
% filters{end+1} = struct('type', 'norm', 'size', norm_filt_size, 'max_gain', 1/32);
%% input parameters: plotting features
plt_opts = struct();
plt_opts.c_lim = [0 1]*256;
plt_opts.font_size = 24; % figure font size
plt_opts.b_plot_src = false; % plot source images?
plt_opts.b_plot_u = false; % plot u component field?
plt_opts.b_plot_v = false; % plot v component field?
plt_opts.b_plot_cv = false; % plot correlation value?
plt_opts.font_size = 24; % figure font size
plt_opts.b_plot_src = true; % plot source images?
plt_opts.b_plot_u = true; % plot u component field?
plt_opts.b_plot_v = true; % plot v component field?
plt_opts.b_plot_cv = true; % plot correlation value?
%% check job directory
job_completed_dir = [job_dir '/completed/'];
......@@ -101,6 +105,11 @@ if exist(job_file, 'file')
error('will not overwrite job');
end
end
% make output directory
if ~exist(vec_dir, 'dir')
fprintf('making output directory ...\n');
mkdir(vec_dir);
end
%% load sample and check all files in range exist
n_src = length(src_range);
......@@ -187,6 +196,8 @@ job = struct();
job.job_idx = job_idx;
job.job_dir = job_dir;
job.job_pat = job_pat;
% timeseries flag
job.b_timeseries= b_timeseries;
% source images
job.src_dir = src_dir;
job.src_pat = src_pat;
......
job_execute.m
View file @ 00b867fe
......@@ -8,6 +8,8 @@ function job_execute(job_file)
src_pat = job.src_pat;
src_range = job.src_range;
n_src = length(src_range);
% timeseries PIV flag
b_timeseries = job.b_timeseries;
% background image
b_use_bg = job.b_use_bg;
bg_dir = job.bg_dir;
......@@ -89,8 +91,13 @@ function job_execute(job_file)
% read attributes
atts = readAttributes(imx);
s = atts('Reference time dt 1');
piv_dt = 1E-6 * s.value;
if isKey(atts, 'Reference time dt 1')
s = atts('Reference time dt 1');
piv_dt = 1E-6 * s.value;
else
warning('dt field is missing from IM7 file, dt = 1');
piv_dt = 1;
end
t_load = toc(t_samp);
%% preprocess images
......@@ -109,17 +116,45 @@ function job_execute(job_file)
end
t_preproc = toc(t_preproc);
%% PIV cross-correlation
%% create image pairs
im_pairs = cell(2, n_correl);
im_correl_mask = cell(1, n_correl);
if b_timeseries
%% time-series cross-correlation
% preload first image
if n == 1
im_filt_prev= im_filt;
continue;
end
% allocate images from timeseries to image pairs
for c = 1 : n_correl
iA = piv_opts(c).i_frames(1);
iB = piv_opts(c).i_frames(2);
im_pairs(1,c) = im_filt_prev(iA);
im_pairs(2,c) = im_filt(iB);
im_correl_mask{c} = im_mask_ary{iA} | im_mask_ary{iB};
end
% save current pair for next iteration
im_filt_prev = im_filt;
else
%% regular PIV
for c = 1 : n_correl
iA = piv_opts(c).i_frames(1);
iB = piv_opts(c).i_frames(2);
im_pairs(1,c) = im_filt(iA);
im_pairs(2,c) = im_filt(iB);
im_correl_mask{c} = im_mask_ary{iA} | im_mask_ary{iB};
end
end
%% cross-corrleate
piv_result = cell(1,n_correl);
t_proc = tic;
for c = 1 : n_correl
%% get images to cross-correlate
iA = piv_opts(c).i_frames(1);
iB = piv_opts(c).i_frames(2);
im_A = im_filt{iA};
im_B = im_filt{iB};
im_A = im_pairs{1,c};
im_B = im_pairs{2,c};
%% plot source, if requested
if plt_opts.b_plot_src
figure(10);
......@@ -128,11 +163,11 @@ function job_execute(job_file)
axis equal tight ij;
drawnow;
end
%% cross-correlate
fprintf(' correlation %d of %d\n', c, n_correl);
piv_result{c} = PIV_2D_wdef(im_A, im_B, ...
im_mask_ary{iA} | im_mask_ary{iB}, ...
im_correl_mask{c}, ...
piv_opts(c));
end
t_proc = toc(t_proc);
......@@ -211,6 +246,11 @@ function job_execute(job_file)
ax(c) = gca;
imagesc(V.win_x, V.win_y, V.peak_mag')
axis equal tight ij;
hold on;
[xm,ym] = ndgrid(V.win_x, V.win_y);
ir = 1 : 4 : V.n_windows(1);
jr = 1 : 4 : V.n_windows(2);
quiversc(xm(ir,jr),ym(ir,jr),V.ux(ir,jr),V.uy(ir,jr),10,'k');
colormap(jet);
colorbar;
set(gca,'clim',[0 1]);
......
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