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https://gitcode.com/gh_mirrors/ope/OpenFace.git
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156 lines
4.5 KiB
Matlab
156 lines
4.5 KiB
Matlab
function Script_CLNF_cross_data_general()
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addpath(genpath('../'));
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% Replace this with the location of the Menpo dataset
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[images, detections, labels] = Collect_menpo_imgs('D:\Datasets\menpo/');
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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_Patch_Experts( '../models/general/', 'ccnf_patches_*_general.mat', [], [], clmParams);
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%% Fitting the model to the provided image
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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 = [35, 27, 20, 20];
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clmParams.sigmaMeanShift = [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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% Loading the final scale
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[clmParams_inner, pdm_inner] = Load_CLM_params_inner();
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clmParams_inner.window_size = [17,17;19,19;21,21;23,23];
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inds_inner = 18:68;
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[patches_inner] = Load_Patch_Experts( '../models/general/', 'ccnf_patches_*general_no_out.mat', [], [], clmParams_inner);
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clmParams_inner.multi_modal_types = patches_inner(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 = cell(numel(images), 1);
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labels_all = cell(numel(images), 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 = false; % set to true to visualise the fitting
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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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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(size(views,1),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_ind);
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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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% Perform inner face fitting
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shape_inner = shape(inds_inner,:);
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[ 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);
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if(a > 0.9)
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g_param = [a; Rot2Euler(R)'; T];
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bbox_2 = [min(shape_inner(:,1)), min(shape_inner(:,2)), max(shape_inner(:,1)), max(shape_inner(:,2))];
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[shape_inner] = Fitting_from_bb(image, [], bbox_2, pdm_inner, patches_inner, clmParams_inner, 'gparam', g_param, 'lparam', l_params);
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% Now after detections incorporate the eyes back
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% into the face model
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shape(inds_inner, :) = shape_inner;
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[ ~, ~, ~, ~, ~, ~, shape_fit] = fit_PDM_ortho_proj_to_2D_no_reg(pdm.M, pdm.E, pdm.V, shape);
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all_lmark_lhoods(:,i) = lmark_lhood;
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all_views_used(i) = view_used;
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shape = shape_fit;
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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(verbose)
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v_points = logical(patches(1).visibilities(view_used,:))';
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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.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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%%
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output_results = 'results/results_clnf_cross-data_general.mat';
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save(output_results, 'experiment');
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end
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