DCLM more results - JANUS and 300W.

matlab_version/experiments_JANUS/Script_DCLM_general_large.m

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+function Script_DCLM_general_large()
+
+addpath('../PDM_helpers/');
+addpath('../fitting/normxcorr2_mex_ALL');
+addpath('../fitting/');
+addpath('../CCNF/');
+addpath('../models/');
+
+% Replace this with the location of in 300 faces in the wild data
+root_test_data = 'D:/Datasets/janus_labeled';
+
+[images, detections, labels] = Collect_JANUS_imgs(root_test_data);
+
+%% loading the patch experts
+   
+clmParams = struct;
+
+clmParams.window_size = [29,29; 27,27; 21,21; 21,21];
+clmParams.numPatchIters = size(clmParams.window_size,1);
+
+[patches] = Load_DCLM_Patch_Experts( '../models/general/', 'dccnf_patches_*_general.mat', [], [], clmParams);
+
+%% Fitting the model to the provided image
+
+output_root = './wild_fit_dclm/';
+
+% the default PDM to use
+pdmLoc = ['../models/pdm/pdm_68_aligned_wild.mat'];
+
+load(pdmLoc);
+
+pdm = struct;
+pdm.M = double(M);
+pdm.E = double(E);
+pdm.V = double(V);
+
+clmParams.regFactor = [35, 27, 20, 20];
+clmParams.sigmaMeanShift = [1.25, 1.375, 1.5, 1.5]; 
+clmParams.tikhonov_factor = [2.5, 5, 7.5, 7.5];
+
+clmParams.startScale = 1;
+clmParams.num_RLMS_iter = 10;
+clmParams.fTol = 0.01;
+clmParams.useMultiScale = true;
+clmParams.use_multi_modal = 1;
+clmParams.multi_modal_types  = patches(1).multi_modal_types;
+clmParams.numPatchIters = 4;
+
+% for recording purposes
+experiment.params = clmParams;
+
+num_points = numel(M)/3;
+
+shapes_all = zeros(size(labels,2),size(labels,3), size(labels,1));
+labels_all = zeros(size(labels,2),size(labels,3), size(labels,1));
+lhoods = zeros(numel(images),1);
+all_lmark_lhoods = zeros(num_points, numel(images));
+all_views_used = zeros(numel(images),1);
+
+% Use the multi-hypothesis model, as bounding box tells nothing about
+% orientation
+multi_view = true;
+verbose = false;
+tic
+for i=1:numel(images)
+
+    image = imread(images(i).img);
+    image_orig = image;
+    
+    if(size(image,3) == 3)
+        image = rgb2gray(image);
+    end              
+
+    bbox = detections(i,:);                  
+    
+    % have a multi-view version
+    if(multi_view)
+
+        views = [0,0,0; 0,-45,0; -30,0,0; 0,45,0; 30,0,0];
+        views = views * pi/180;                                                                                     
+
+        shapes = zeros(num_points, 2, size(views,1));
+        ls = zeros(size(views,1),1);
+        lmark_lhoods = zeros(num_points,size(views,1));
+        views_used = zeros(num_points,size(views,1));
+
+        % Find the best orientation
+        for v = 1:size(views,1)
+            [shapes(:,:,v),~,~,ls(v),lmark_lhoods(:,v),views_used(v)] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams, 'orientation', views(v,:));                                            
+        end
+
+        [lhood, v_ind] = max(ls);
+        lmark_lhood = lmark_lhoods(:,v_ind);
+
+        shape = shapes(:,:,v_ind);
+        view_used = views_used(v);
+
+    else
+        [shape,~,~,lhood,lmark_lhood,view_used] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams);
+    end
+
+    all_lmark_lhoods(:,i) = lmark_lhood;
+    all_views_used(i) = view_used;
+
+    shapes_all(:,:,i) = shape;
+    labels_all(:,:,i) = labels(i,:,:);
+
+    if(mod(i, 50)==0)
+        fprintf('%d done\n', i );
+    end
+
+    lhoods(i) = lhood;
+
+    if(verbose)
+
+        actualShape = squeeze(labels(i,:,:));
+        
+        [height_img, width_img,~] = size(image_orig);
+        width = max(actualShape(:,1)) - min(actualShape(:,1));
+        height = max(actualShape(:,2)) - min(actualShape(:,2));
+
+        v_points = sum(squeeze(labels(i,:,:)),2) > 0;
+
+        img_min_x = max(int32(min(actualShape(v_points,1))) - width/3,1);
+        img_max_x = min(int32(max(actualShape(v_points,1))) + width/3,width_img);
+
+        img_min_y = max(int32(min(actualShape(v_points,2))) - height/3,1);
+        img_max_y = min(int32(max(actualShape(v_points,2))) + height/3,height_img);
+
+        shape(:,1) = shape(:,1) - double(img_min_x);
+        shape(:,2) = shape(:,2) - double(img_min_y);
+
+        image_orig = image_orig(img_min_y:img_max_y, img_min_x:img_max_x, :);    
+
+        % valid points to draw (not to draw
+        % occluded ones)
+
+%         f = figure('visible','off');
+        f = figure;
+        try
+        if(max(image_orig(:)) > 1)
+            imshow(double(image_orig)/255, 'Border', 'tight');
+        else
+            imshow(double(image_orig), 'Border', 'tight');
+        end
+        axis equal;
+        hold on;
+        
+        plot(shape(:,1), shape(:,2),'.r','MarkerSize',20);
+        plot(shape(:,1), shape(:,2),'.b','MarkerSize',10);
+%                                         print(f, '-r80', '-dpng', sprintf('%s/%s%d.png', output_root, 'fit', i));
+%         print(f, '-djpeg', sprintf('%s/%s%d.jpg', output_root, 'fit', i));
+%                                         close(f);
+        hold off;
+%         drawnow expose
+        close(f);
+        catch warn
+
+        end
+    end
+
+end
+toc
+
+experiment.errors_normed = compute_error(labels_all, shapes_all + 0.5);
+experiment.lhoods = lhoods;
+experiment.shapes = shapes_all;
+experiment.labels = labels_all;
+experiment.all_lmark_lhoods = all_lmark_lhoods;
+experiment.all_views_used = all_views_used;
+% save the experiment
+if(~exist('experiments', 'var'))
+    experiments = experiment;
+else
+    experiments = cat(1, experiments, experiment);
+end
+fprintf('experiment %d done: mean normed error %.3f median normed error %.4f\n', ...
+    numel(experiments), mean(experiment.errors_normed), median(experiment.errors_normed));
+
+%%
+output_results = 'results/results_wild_dclm_general_large.mat';
+save(output_results, 'experiments');
+    
+end