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146 lines
4.0 KiB
Matlab
146 lines
4.0 KiB
Matlab
function Script_CECLM_general()
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addpath('../PDM_helpers/');
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addpath('../fitting/normxcorr2_mex_ALL');
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addpath('../fitting/');
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addpath('../CCNF/');
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addpath('../models/');
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% Replace this with the location of in 300 faces in the wild data
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root_test_data = 'D:/Datasets/janus_labeled';
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[images, detections, labels] = Collect_JANUS_imgs(root_test_data);
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%% loading the patch experts
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clmParams = struct;
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clmParams.window_size = [25,25; 23,23; 21,21; 21,21];
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clmParams.numPatchIters = size(clmParams.window_size,1);
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[patches] = Load_DCLM_Patch_Experts( '../models/cen/', 'cen_patches_*_general.mat', [], [], clmParams);
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%% Fitting the model to the provided image
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output_root = './fit_ceclm_general/';
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% the default PDM to use
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pdmLoc = ['../models/pdm/pdm_68_aligned_wild.mat'];
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load(pdmLoc);
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pdm = struct;
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pdm.M = double(M);
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pdm.E = double(E);
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pdm.V = double(V);
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clmParams.regFactor = 0.9*[35, 27, 20, 20];
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clmParams.sigmaMeanShift = 1.5*[1.25, 1.375, 1.5, 1.5];
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clmParams.tikhonov_factor = [2.5, 5, 7.5, 7.5];
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clmParams.startScale = 1;
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clmParams.num_RLMS_iter = 10;
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clmParams.fTol = 0.01;
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clmParams.useMultiScale = true;
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clmParams.use_multi_modal = 1;
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clmParams.multi_modal_types = patches(1).multi_modal_types;
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clmParams.numPatchIters = 4;
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% for recording purposes
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experiment.params = clmParams;
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num_points = numel(M)/3;
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shapes_all = zeros(size(labels,2),size(labels,3), size(labels,1));
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labels_all = zeros(size(labels,2),size(labels,3), size(labels,1));
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lhoods = zeros(numel(images),1);
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all_lmark_lhoods = zeros(num_points, numel(images));
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all_views_used = zeros(numel(images),1);
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% Use the multi-hypothesis model, as bounding box tells nothing about
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% orientation
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multi_view = true;
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verbose = true;
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output_img = false;
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if(output_img)
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output_root = './ceclm_gen_out/';
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if(~exist(output_root, 'dir'))
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mkdir(output_root);
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end
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end
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if(verbose)
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f = figure;
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end
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% As the orientations are not equally reliable reweigh them
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load('../learn_error_mapping/cen_general_mapping.mat');
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tic
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for i=1:numel(images)
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image = imread(images(i).img);
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image_orig = image;
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if(size(image,3) == 3)
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image = rgb2gray(image);
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end
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bbox = detections(i,:);
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% have a multi-view version
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if(multi_view)
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views = [0,0,0; 0,-30,0; 0,30,0; 0,-55,0; 0,55,0; 0,0,30; 0,0,-30; 0,-90,0; 0,90,0; 0,-70,40; 0,70,-40];
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views = views * pi/180;
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[shape,~,~,lhood,lmark_lhood,view_used] =...
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Fitting_from_bb_multi_hyp(image, [], bbox, pdm, patches, clmParams, views, early_term_params);
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else
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[shape,~,~,lhood,lmark_lhood,view_used] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams);
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end
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all_lmark_lhoods(:,i) = lmark_lhood;
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all_views_used(i) = view_used;
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shapes_all(:,:,i) = shape;
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labels_all(:,:,i) = labels(i,:,:);
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if(mod(i, 200)==0)
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fprintf('%d done\n', i );
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end
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lhoods(i) = lhood;
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if(output_img)
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v_points = sum(squeeze(labels(i,:,:)),2) > 0;
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DrawFaceOnImg(image_orig, shape, sprintf('%s/%s%d.jpg', output_root, 'fit', i), bbox, v_points);
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end
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if(verbose)
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v_points = sum(squeeze(labels(i,:,:)),2) > 0;
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DrawFaceOnFig(image_orig, shape, bbox, v_points);
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end
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end
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toc
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experiment.errors_normed = compute_error(labels_all, shapes_all - 0.5);
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experiment.lhoods = lhoods;
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experiment.shapes = shapes_all;
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experiment.labels = labels_all;
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experiment.all_lmark_lhoods = all_lmark_lhoods;
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experiment.all_views_used = all_views_used;
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% save the experiment
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if(~exist('experiments', 'var'))
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experiments = experiment;
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else
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experiments = cat(1, experiments, experiment);
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end
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fprintf('experiment %d done: mean normed error %.3f median normed error %.4f\n', ...
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numel(experiments), mean(experiment.errors_normed), median(experiment.errors_normed));
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%%
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output_results = 'results/results_ceclm_general.mat';
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save(output_results, 'experiments');
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end
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