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https://gitcode.com/gh_mirrors/ope/OpenFace.git
synced 2026-04-25 05:30:16 +00:00
151 lines
4.1 KiB
Matlab
151 lines
4.1 KiB
Matlab
function Script_CLM()
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addpath(genpath('../'));
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% Replace this with the location of in 300 faces in the wild data
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if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
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root_test_data = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];
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else
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root_test_data = 'D:/Dropbox/Dropbox/AAM/test data/';
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end
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[images, detections, labels] = Collect_wild_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;];
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clmParams.numPatchIters = size(clmParams.window_size,1);
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[patches] = Load_Patch_Experts( '../models/wild/', 'svr_patches_*_wild.mat', [], [], clmParams);
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%% Fitting the model to the provided image
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% Change if you want to visualize the outputs
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verbose = false;
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output_img = false;
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if(output_img)
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output_root = './clm_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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% 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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% the default model parameters to use
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clmParams.regFactor = [35, 27, 20, 5];
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clmParams.sigmaMeanShift = [1.25, 1.375, 1.5, 1.75];
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clmParams.tikhonov_factor = [2.5, 5, 7.5, 12.5];
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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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% 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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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; 0,0,30; 0,0,-30;];
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views = views * pi/180;
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shapes = zeros(num_points, 2, size(views,1));
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ls = zeros(size(views,1),1);
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lmark_lhoods = zeros(num_points,size(views,1));
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views_used = zeros(num_points,size(views,1));
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% Find the best orientation
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for v = 1:size(views,1)
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[shapes(:,:,v),~,~,ls(v),lmark_lhoods(:,v),views_used(v)] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams, 'orientation', views(v,:));
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end
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[lhood, v_ind] = max(ls);
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lmark_lhood = lmark_lhoods(:,v_ind);
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shape = shapes(:,:,v_ind);
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view_used = views_used(v);
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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, 100)==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 + 1.0);
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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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fprintf('Done: mean normed error %.3f median normed error %.4f\n', ...
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mean(experiment.errors_normed), median(experiment.errors_normed));
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%%
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output_results = 'results/results_clm.mat';
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save(output_results, 'experiment');
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end
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