2128589309
- New AU recognition models trained on extra datasets - Bosphorus, UNBC, FERA2011 - Cleaner and clearer separation of static and dynamic AU models - AU training code cleaned up and instructions added - bug fixes with median feature computation - AU prediction correction (smoothing and shifting) with post processing
137 lines
No EOL
3.4 KiB
Matlab
137 lines
No EOL
3.4 KiB
Matlab
function [ err_outline, err_no_outline ] = Run_CLM_fitting_on_images(output_loc, database_root, varargin)
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%RUN_CLM_FITTING_ON_IMAGES Summary of this function goes here
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% Detailed explanation goes here
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dataset_dirs = {};
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if(any(strcmp(varargin, 'use_afw')))
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afw_loc = [database_root '/AFW/'];
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dataset_dirs = cat(1, dataset_dirs, afw_loc);
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end
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if(any(strcmp(varargin, 'use_lfpw')))
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lfpw_loc = [database_root 'lfpw/testset/'];
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dataset_dirs = cat(1, dataset_dirs, lfpw_loc);
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end
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if(any(strcmp(varargin, 'use_ibug')))
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ibug_loc = [database_root 'ibug/'];
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dataset_dirs = cat(1, dataset_dirs, ibug_loc);
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end
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if(any(strcmp(varargin, 'use_helen')))
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helen_loc = [database_root '/helen/testset/'];
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dataset_dirs = cat(1, dataset_dirs, helen_loc);
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end
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if(any(strcmp(varargin, 'verbose')))
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verbose = true;
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else
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verbose = false;
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end
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command = '"../../x64/Release/FaceLandmarkImg.exe" ';
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if(any(strcmp(varargin, 'model')))
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model = varargin{find(strcmp(varargin, 'model')) + 1};
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else
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% the default model is the 68 point in the wild one
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model = '"model/main_wild.txt"';
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end
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if(any(strcmp(varargin, 'multi_view')))
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multi_view = varargin{find(strcmp(varargin, 'multi_view')) + 1};
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else
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multi_view = 0;
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end
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command = cat(2, command, [' -mloc ' model ' ']);
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command = cat(2, command, [' -multi_view ' num2str(multi_view) ' ']);
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tic
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parfor i=1:numel(dataset_dirs)
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input_loc = ['-fdir "', dataset_dirs{i}, '" '];
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command_c = cat(2, command, input_loc);
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out_loc = ['-ofdir "', output_loc, '" '];
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command_c = cat(2, command_c, out_loc);
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if(verbose)
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out_im_loc = ['-oidir "', output_loc, '" '];
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command_c = cat(2, command_c, out_im_loc);
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end
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command_c = cat(2, command_c, ' -wild ');
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dos(command_c);
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end
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toc
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%%
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% Extract the error sizes
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dirs = {[database_root '/AFW/'];
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[database_root '/ibug/'];
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[database_root '/helen/testset/'];
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[database_root 'lfpw/testset/'];};
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landmark_dets = dir([output_loc '/*.pts']);
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landmark_det_dir = [output_loc '/'];
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num_imgs = size(landmark_dets,1);
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labels = zeros(68,2,num_imgs);
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shapes = zeros(68,2,num_imgs);
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curr = 0;
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for i=1:numel(dirs)
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gt_labels = dir([dirs{i}, '*.pts']);
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for g=1:numel(gt_labels)
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curr = curr+1;
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gt_landmarks = dlmread([dirs{i}, gt_labels(g).name], ' ', 'A4..B71');
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% find the corresponding detection
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landmark_det = dlmread([landmark_det_dir, gt_labels(g).name], ' ', 'A4..B71');
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labels(:,:,curr) = gt_landmarks;
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if(size(landmark_det,1) == 66)
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inds_66 = [[1:60],[62:64],[66:68]];
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shapes(inds_66,:,curr) = landmark_det;
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else
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shapes(:,:,curr) = landmark_det;
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end
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end
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end
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% Convert to correct format, so as to have same feature points in ground
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% truth and detections
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if(size(shapes,2) == 66 && size(labels,2) == 68)
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inds_66 = [[1:60],[62:64],[66:68]];
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labels = labels(inds_66,:,:);
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shapes = shapes(inds_66,:,:);
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end
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% Center the pixel
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labels = labels - 0.5;
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err_outline = compute_error(labels, shapes);
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labels_no_out = labels(18:end,:,:);
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shapes_no_out = shapes(18:end,:,:);
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err_no_outline = compute_error(labels_no_out, shapes_no_out);
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%%
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save([output_loc, 'res.mat'], 'labels', 'shapes', 'err_outline', 'err_no_outline');
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end |