xdic-dev · johnaoga · Apr 5, 2025
diff --git a/.gitignore b/.gitignore
@@ -0,0 +1,2 @@
+logs
+analysis
diff --git a/src/lib/compute_initial_seed_points.m b/src/lib/compute_initial_seed_points.m
@@ -0,0 +1,18 @@
+function [initial_seed_point_set1, refmask_trial] = compute_initial_seed_points(cam_first_satur, cam_second_satur)
+    % This function computes initial seed points for DIC analysis
+    % It is extracted from stepD_2DDIC.m to support parallel processing
+
+    % Initialize output
+    initial_seed_point_set1 = struct();
+
+    % Get size of images
+    siz = size(cam_first_satur);
+
+    % Create initial mask (full image)
+    refmask_trial = true(siz(1:2));
+
+    % Set initial seed point in the center of the image
+    % This is a simple approach - you might want to make this more sophisticated
+    initial_seed_point_set1.sw = [siz(2)/2, siz(1)/2];
+    initial_seed_point_set1.pw = initial_seed_point_set1.sw;
+end
diff --git a/src/lib/compute_matching.m b/src/lib/compute_matching.m
@@ -0,0 +1,105 @@
+function [refmask_trial_matched, initial_seed_point_set2] = compute_matching(data_path, dic_path, subject_id, material, trial, stereopair, phase, showvisu, idx_frame_start, idx_frame_end, frame_jump, ref_trial_id, automatic_process)
+    % This function handles the matching step between stereo pairs
+    % It is extracted from stepD_2DDIC.m to enable parallel processing
+
+    % Get parameters from original function
+    TRUE_FPS = 50;
+    if str2double(cell2mat(regexp(subject_id, '\d+', 'match'))) < 8
+        LIMIT_GRAYSCALE = 70;
+    else
+        LIMIT_GRAYSCALE = 100;
+    end
+
+    % Ncorr DIC parameters for matching
+    analysis_direction = 'regular';
+    subset_radius_ncorr_matching = 60;
+    subset_spacing = 10;
+    cutoff_matching = 1e-5;
+    number_iteration_solver = 100;
+    number_threads = 1;
+    high_strain_analysis = 1;
+    seed_propagation = 'seed';
+    auto_ref_change = 1;
+    step_ref_change = 10;
+
+    % Set up parameters struct
+    step1_2_parameters = struct(...
+        'type', analysis_direction,...
+        'radius', subset_radius_ncorr_matching,...
+        'spacing', subset_spacing,...
+        'cutoff_diffnorm', cutoff_matching,...
+        'num_threads', number_threads,...
+        'enabled_stepanalysis', high_strain_analysis,...
+        'seedpropagation', seed_propagation,...
+        'autorefchange', auto_ref_change,...
+        'steprefchange', step_ref_change);
+
+    base_parameters = struct(...
+        'num_iterations', number_iteration_solver);
+
+    % Read video data and process images
+    [cam_first_raw, cam_second_raw, cam_1, cam_2] = import_vid(data_path,...
+        'subject', subject_id,...
+        'material', material,...
+        'trial', trial,...
+        'stereopair', stereopair,...
+        'phase', phase,...
+        'idxstart_set', idx_frame_start,...
+        'idxend_set', idx_frame_end,...
+        'framejump', frame_jump);
+
+    if strcmp(phase, "slide1")
+        cam_first_raw = cam_first_raw(:,:,1:end/2+5);
+        cam_second_raw = cam_second_raw(:,:,1:end/2+5);
+    end
+
+    % Image saturation
+    cam_first_satur = satur(cam_first_raw, 'level', LIMIT_GRAYSCALE);
+    cam_second_satur = satur(cam_second_raw, 'level', LIMIT_GRAYSCALE);
+
+    % Get initial seed points (this would need to be passed or computed)
+    [initial_seed_point_set1, refmask_trial] = compute_initial_seed_points(cam_first_satur, cam_second_satur);
+
+    % Create output directory
+    outputPath = fullfile(dic_path, subject_id, material, trial, phase);
+    if ~exist(outputPath, 'dir')
+        mkdir(outputPath);
+    end
+    base_parameters.outputPath = outputPath;
+
+    % MATCHING STEP
+    step1_2_parameters.initial_seed = initial_seed_point_set1.pw;
+    siz = size(cam_first_raw);
+    input1 = cam_first_satur(:,:,1);
+    input2 = zeros(siz(1), siz(2), 2, 'uint8');
+    input2(:,:,1) = cam_second_satur(:,:,1);
+    input2(:,:,2) = cam_first_satur(:,:,1);
+
+    % Save info before ncorr analysis
+    savefileName = fullfile(outputPath, sprintf('dic_info_data_target_pair%d.mat', stereopair));
+    actual_fps_meas = TRUE_FPS/frame_jump;
+    idxframe = idx_frame_start:frame_jump:idx_frame_end;
+    save(savefileName, 'actual_fps_meas', 'idxframe');
+
+    % Run ncorr analysis
+    [h12, file_logic] = ncorr_dic_rewrited('cam_number', [cam_1, cam_2],...
+        'cam_data_ref', input1,...
+        'cam_data_cur', input2,...
+        'mask', refmask_trial,...
+        'automatic_process', automatic_process,...
+        'step_param', step1_2_parameters,...
+        'base_param', base_parameters);
+
+    % Get results
+    refmask_trial_matched = h12.current(1).roi.mask;
+    U_mapped = h12.data_dic.displacements(1).plot_u_ref_formatted/(subset_spacing+1);
+    V_mapped = h12.data_dic.displacements(1).plot_v_ref_formatted/(subset_spacing+1);
+
+    initial_seed_point_set2.sw = map_pointcoordinate(initial_seed_point_set1.sw, {U_mapped, V_mapped});
+    initial_seed_point_set2.pw = map_subset2pixel(initial_seed_point_set2.sw, subset_spacing);
+
+    if ~file_logic
+        close(h12.handles_gui.figure);
+    end
+    clear('h12');
+end
diff --git a/src/lib/compute_tracking.m b/src/lib/compute_tracking.m
@@ -0,0 +1,88 @@
+function success = compute_tracking(data_path, dic_path, subject_id, material, trial, stereopair, phase, showvisu, idx_frame_start, idx_frame_end, frame_jump, ref_trial_id, automatic_process, track_id, refmask, initial_seed_point)
+    % This function handles the tracking step for each camera
+    % It is extracted from stepD_2DDIC.m to enable parallel processing
+    % track_id: 1 for first camera, 2 for second camera
+
+    % Get parameters from original function
+    TRUE_FPS = 50;
+    if str2double(cell2mat(regexp(subject_id, '\d+', 'match'))) < 8
+        LIMIT_GRAYSCALE = 70;
+    else
+        LIMIT_GRAYSCALE = 100;
+    end
+
+    % Ncorr DIC parameters for tracking
+    analysis_direction = 'regular';
+    subset_radius_ncorr_tracking = 40;
+    subset_spacing = 10;
+    cutoff_tracking = 1e-5;
+    number_iteration_solver = 100;
+    number_threads = 1;
+    high_strain_analysis = 1;
+    seed_propagation = 'seed';
+    auto_ref_change = 1;
+    step_ref_change = 10;
+
+    % Set up parameters struct
+    step_parameters = struct(...
+        'type', analysis_direction,...
+        'radius', subset_radius_ncorr_tracking,...
+        'spacing', subset_spacing,...
+        'cutoff_diffnorm', cutoff_tracking,...
+        'num_threads', number_threads,...
+        'enabled_stepanalysis', high_strain_analysis,...
+        'seedpropagation', seed_propagation,...
+        'autorefchange', auto_ref_change,...
+        'steprefchange', step_ref_change);
+
+    base_parameters = struct(...
+        'num_iterations', number_iteration_solver);
+
+    % Read video data and process images
+    [cam_first_raw, cam_second_raw, cam_1, cam_2] = import_vid(data_path,...
+        'subject', subject_id,...
+        'material', material,...
+        'trial', trial,...
+        'stereopair', stereopair,...
+        'phase', phase,...
+        'idxstart_set', idx_frame_start,...
+        'idxend_set', idx_frame_end,...
+        'framejump', frame_jump);
+
+    if strcmp(phase, "slide1")
+        cam_first_raw = cam_first_raw(:,:,1:end/2+5);
+        cam_second_raw = cam_second_raw(:,:,1:end/2+5);
+    end
+
+    % Image saturation
+    cam_first_satur = satur(cam_first_raw, 'level', LIMIT_GRAYSCALE);
+    cam_second_satur = satur(cam_second_raw, 'level', LIMIT_GRAYSCALE);
+
+    % TRACKING STEP
+    step_parameters.initial_seed = initial_seed_point.pw;
+
+    if track_id == 1
+        [h, file_logic] = ncorr_dic_rewrited('cam_number', cam_1,...
+            'cam_data_ref', cam_first_satur(:,:,1),...
+            'cam_data_cur', cam_first_satur(:,:,1:end),...
+            'mask', refmask,...
+            'automatic_process', automatic_process,...
+            'step_param', step_parameters,...
+            'base_param', base_parameters);
+    else
+        [h, file_logic] = ncorr_dic_rewrited('cam_number', cam_2,...
+            'cam_data_ref', cam_second_satur(:,:,1),...
+            'cam_data_cur', cam_second_satur(:,:,2:end),...
+            'mask', refmask,...
+            'automatic_process', automatic_process,...
+            'step_param', step_parameters,...
+            'base_param', base_parameters);
+    end
+
+    if ~file_logic
+        close(h.handles_gui.figure);
+    end
+    clear('h');
+
+    success = file_logic;
+end
diff --git a/src/lib/dic_2d_analysis.m b/src/lib/dic_2d_analysis.m
@@ -1,30 +1,108 @@
 function output = dic_2d_analysis(trial_target, data_path, dic_path, subject_id, phase_id, material, num_pair, nfcond_set, spddxlcond_set, calib_folder_set, ref_trial_id, idx_frame_start, idx_frame_end, frame_jump, showvisu, debug_mode, automatic_process)
     fprintf('Begin of %s\n', mfilename);
+
     % Flatten all combinations (trial_jj, stereopair_kk)
-    pairs_trials = combvec( 1:num_pair, trial_target)';
-
-    % parfor over the flattened set of combinations
-    output = cell(size(pairs_trials, 1), 1);
-    parfor i = 1:size(pairs_trials, 1)
+    pairs_trials = combvec(1:num_pair, trial_target)';
+    num_combinations = size(pairs_trials, 1);
+
+    % Initialize storage for matching results
+    matching_results = cell(num_combinations, 1);
+
+    % STEP 1: Parallel matching computation
+    fprintf('Starting parallel matching computation...\n');
+    parfor i = 1:num_combinations
         pair_id = pairs_trials(i, 1);
         trial_id = pairs_trials(i, 2);
-        fprintf('Trial ID: %d, Pair ID: %d\n', trial_id, pair_id);
-        %output{i} = {sprintf("Trial ID: %d, Pair ID: %d", trial_id, pair_id), 'success', true};
-        [o1, o2, o3]  = stepD_2DDIC( ...
-            'baseDataPath', data_path, ...
-            'baseResultPath', dic_path, ...
-            'subject', subject_id, ...
-            'material', material, ...
-            'trial', sprintf("%03d", trial_id), ...
-            'stereopair', pair_id, ...
-            'phase', phase_id, ...
-            'showvisu', showvisu, ...
-            'idxstart_set', idx_frame_start, ...
-            'idxend_set', idx_frame_end, ...
-            'reftrial_setmanual', sprintf("%03d", ref_trial_id), ...
-            'jump', frame_jump, ...
-            'automatic_process', automatic_process);
-        output{i} = [o1, o2, o3];
+        fprintf('Matching: Trial ID: %d, Pair ID: %d\n', trial_id, pair_id);
+
+        % Compute matching between stereo pairs
+        [refmask_trial_matched, initial_seed_point_set2] = compute_matching(...
+            data_path, dic_path, subject_id, material, ...
+            sprintf('%03d', trial_id), pair_id, phase_id, ...
+            showvisu, idx_frame_start, idx_frame_end, frame_jump, ...
+            ref_trial_id, automatic_process);
+
+        matching_results{i} = struct(...
+            'trial_id', trial_id, ...
+            'pair_id', pair_id, ...
+            'refmask_trial_matched', refmask_trial_matched, ...
+            'initial_seed_point_set2', initial_seed_point_set2);
     end
+
+    % STEP 2: Parallel tracking computation
+    fprintf('Starting parallel tracking computation...\n');
+    % Create combinations for tracking (trial, pair, track_id)
+    tracking_combinations = [];
+    for i = 1:num_combinations
+        tracking_combinations = [tracking_combinations; ...
+            pairs_trials(i,:), 1; ... % track_id 1 for first camera
+            pairs_trials(i,:), 2];    % track_id 2 for second camera
+    end
+
+    num_tracking_tasks = size(tracking_combinations, 1);
+    tracking_results = cell(num_tracking_tasks, 1);
+
+    parfor i = 1:num_tracking_tasks
+        trial_id = tracking_combinations(i, 2);
+        pair_id = tracking_combinations(i, 1);
+        track_id = tracking_combinations(i, 3);
+
+        fprintf('Tracking: Trial ID: %d, Pair ID: %d, Track ID: %d\n', ...
+            trial_id, pair_id, track_id);
+
+        % Find matching results for this trial/pair
+        matching_idx = -1;
+        for j = 1:numel(matching_results)
+            if matching_results{j}.trial_id == trial_id && matching_results{j}.pair_id == pair_id
+                matching_idx = j;
+                break;
+            end
+        end
+        match_data = matching_results{matching_idx};
+
+        % Get appropriate mask and seed point based on track_id
+        if track_id == 1
+            refmask = match_data.refmask_trial_matched;
+            initial_seed_point = match_data.initial_seed_point_set2;
+        else
+            refmask = match_data.refmask_trial_matched;
+            initial_seed_point = match_data.initial_seed_point_set2;
+        end
+
+        % Compute tracking
+        success = compute_tracking(...
+            data_path, dic_path, subject_id, material, ...
+            sprintf('%03d', trial_id), pair_id, phase_id, ...
+            showvisu, idx_frame_start, idx_frame_end, frame_jump, ...
+            ref_trial_id, automatic_process, track_id, ...
+            refmask, initial_seed_point);
+
+        tracking_results{i} = struct(...
+            'trial_id', trial_id, ...
+            'pair_id', pair_id, ...
+            'track_id', track_id, ...
+            'success', success);
+    end
+
+    % Combine results
+    output = cell(num_combinations, 1);
+    for i = 1:num_combinations
+        trial_id = pairs_trials(i, 2);
+        pair_id = pairs_trials(i, 1);
+
+        % Get tracking results for this trial/pair
+        track_success = true;
+        for j = 1:numel(tracking_results)
+            if tracking_results{j}.trial_id == trial_id && tracking_results{j}.pair_id == pair_id
+                track_success = track_success && tracking_results{j}.success;
+            end
+        end
+
+        % Format output as before
+        output{i} = {sprintf('%03d', trial_id), ...
+                    num2str(pair_id), ...
+                    num2str(track_success)};
+    end
+
     fprintf('End of %s\n', mfilename);
 end
diff --git a/src/lib_script/ncorr_dic_rewrited.m b/src/lib_script/ncorr_dic_rewrited.m
@@ -32,15 +32,14 @@
 else
      initial_seed = [];
 end
-showGui = ~automatic_process; 
-handles = ncorr_abr(showGui,initial_seed);
-% handles = ncorr;
+% Use ncorr instead of ncorr_abr for parallel processing
+handles = ncorr;
 handles.set_ref(cam_data_ref);
 handles.set_cur(Myarray2cell(cam_data_cur));
 
 handles.set_roi_ref(mask);
 
-
+% Set DIC parameters
 handles.set_dic_parameters_manually( ...
     step_param.type, ...
     step_param.radius, ...