Cleaning up 300W results and JANUS ones.

matlab_version/experiments_300W/Script_CLNF.m

@@ -0,0 +1,181 @@
+function Script_CLNF()
+
+addpath(genpath('../'));
+
+% Replace this with the location of in 300 faces in the wild data
+if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
+    root_test_data = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];    
+else
+    root_test_data = 'D:/Dropbox/Dropbox/AAM/test data/';
+end
+
+[images, detections, labels] = Collect_wild_imgs(root_test_data);
+%% loading the patch experts and pdms
+   
+clmParams = struct;
+
+clmParams.window_size = [25,25; 23,23; 21,21];
+clmParams.numPatchIters = size(clmParams.window_size,1);
+
+[patches] = Load_Patch_Experts( '../models/wild/', 'ccnf_patches_*_wild.mat', [], [], clmParams);
+
+% 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];
+clmParams.sigmaMeanShift = [1.25, 1.375, 1.5]; 
+clmParams.tikhonov_factor = [2.5, 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;
+   
+% Loading the final scale
+[clmParams_inner, pdm_inner] = Load_CLM_params_inner();
+clmParams_inner.window_size = [17,17;19,19;21,21;23,23];
+inds_inner = 18:68;
+[patches_inner] = Load_Patch_Experts( '../models/general/', 'ccnf_patches_*general_no_out.mat', [], [], clmParams_inner);
+clmParams_inner.multi_modal_types  = patches_inner(1).multi_modal_types;
+
+% for recording purposes
+experiment.params = clmParams;
+
+%% Change if you want to visualize the outputs
+verbose = false;
+output_img = false;
+
+if(output_img)
+    output_root = './clnf_out/';
+    if(~exist(output_root, 'dir'))
+        mkdir(output_root);
+    end
+end
+if(verbose)
+    f = figure;
+end
+
+%% For recording
+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;
+
+%% Fitting the model to the provided image
+
+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,-30,0; 0,30,0; 0,0,30; 0,0,-30;];
+        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
+
+    % Perform inner face fitting
+    shape_inner = shape(inds_inner,:);
+
+    [ a, R, T, ~, l_params, err] = fit_PDM_ortho_proj_to_2D_no_reg(pdm_inner.M, pdm_inner.E, pdm_inner.V, shape_inner);
+    if(a > 0.9)
+        g_param = [a; Rot2Euler(R)'; T];
+
+        bbox_2 = [min(shape_inner(:,1)), min(shape_inner(:,2)), max(shape_inner(:,1)), max(shape_inner(:,2))];
+
+        [shape_inner] = Fitting_from_bb(image, [], bbox_2, pdm_inner, patches_inner, clmParams_inner, 'gparam', g_param, 'lparam', l_params);
+
+        % Now after detections incorporate the eyes back
+        % into the face model
+
+        shape(inds_inner, :) = shape_inner;
+
+        [ ~, ~, ~, ~, ~, ~, shape_fit] = fit_PDM_ortho_proj_to_2D_no_reg(pdm.M, pdm.E, pdm.V, shape);    
+
+        all_lmark_lhoods(:,i) = lmark_lhood;
+        all_views_used(i) = view_used;
+
+        shapes_all(:,:,i) = shape_fit;
+    else
+        shapes_all(:,:,i) = shape;                    
+    end
+    labels_all(:,:,i) = labels(i,:,:);
+
+    if(mod(i, 200)==0)
+        fprintf('%d done\n', i );
+    end
+
+    lhoods(i) = lhood;
+
+    
+    if(output_img)
+        v_points = sum(squeeze(labels(i,:,:)),2) > 0;
+        DrawFaceOnImg(image_orig, shape, sprintf('%s/%s%d.jpg', output_root, 'fit', i), bbox, v_points);
+    end
+    
+    if(verbose)
+        v_points = sum(squeeze(labels(i,:,:)),2) > 0;
+        DrawFaceOnFig(image_orig, shape, bbox, v_points);
+    end
+
+end
+toc
+
+experiment.errors_normed = compute_error(labels_all, shapes_all + 1.0);
+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;
+
+fprintf('Done: mean normed error %.3f median normed error %.4f\n', ...
+    mean(experiment.errors_normed), median(experiment.errors_normed));
+
+%%
+output_results = 'results/results_clnf.mat';
+save(output_results, 'experiment');
+    
+end