mirror of
https://gitcode.com/gh_mirrors/ope/OpenFace.git
synced 2026-05-12 02:12:36 +00:00
97 lines
2.6 KiB
Matlab
97 lines
2.6 KiB
Matlab
function Script_CLNF_general()
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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 = 'F:\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 and pdms
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[ patches, pdm, clmParams ] = Load_CLNF_general();
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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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[ patches_51, pdm_51, clmParams_51, inds_full, inds_inner ] = Load_CLNF_inner();
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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 = './clnf_out_general/';
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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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%% For recording
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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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%% Fitting the model to the provided image
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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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[shape,~,~,lhood,lmark_lhood,view_used] = Fitting_from_bb_multi_hyp(image, [], bbox, pdm, patches, clmParams, views);
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% Perform inner landmark fitting now
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[shape, shape_inner] = Fitting_from_bb_hierarch(image, pdm, pdm_51, patches_51, clmParams_51, shape, inds_full, inds_inner);
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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 + 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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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_clnf_general.mat';
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save(output_results, 'experiment');
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end
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