Fixing eye landmark Matlab demo.
This commit is contained in:
Tadas Baltrusaitis
parent
4a73ece996
commit
e0fa16ee04
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@ -20,3 +20,7 @@ matlab_runners/Feature Point Experiments/yt_features/
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matlab_runners/Feature Point Experiments/yt_features_clm/
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matlab_runners/Gaze Experiments/mpii_out/
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build/
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Release/AU_predictors/
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Release/
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exe/Recording/recording/
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ipch/
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@ -9,22 +9,30 @@ addpath('../CCNF/');
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[clmParams, pdm] = Load_CLM_params_wild();
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[clmParams_eye, pdm_eye] = Load_CLM_params_eye();
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% An accurate CCNF (or CLNF) model
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% [patches] = Load_Patch_Experts( '../models/general/', 'ccnf_patches_*_general.mat', [], [], clmParams);
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% A simpler (but less accurate SVR)
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[patches] = Load_Patch_Experts( '../models/general/', 'svr_patches_*_general.mat', [], [], clmParams);
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[patches_eye] = Load_Patch_Experts( 'C:\Users\Tadas\Dropbox\AAM\patch_experts_eyes\svr_training\trained/', 'svr_patches_*_synth.mat', [], [], clmParams);
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% Loading eye PDM and patch experts
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[clmParams_eye, pdm_right_eye, pdm_left_eye] = Load_CLM_params_eye_28();
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[patches_right_eye] = Load_Patch_Experts( '../models/hierarch/', 'ccnf_patches_*_synth_right_eye.mat', [], [], clmParams_eye);
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[patches_left_eye] = Load_Patch_Experts( '../models/hierarch/', 'ccnf_patches_*_synth_left_eye.mat', [], [], clmParams_eye);
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clmParams_eye.multi_modal_types = patches_right_eye(1).multi_modal_types;
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right_eye_inds = [43,44,45,46,47,48];
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left_eye_inds = [37,38,39,40,41,42];
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right_eye_inds_synth = [9 11 13 15 17 19];
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left_eye_inds_synth = [9 11 13 15 17 19];
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clmParams.multi_modal_types = patches(1).multi_modal_types;
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clmParams_eye.multi_modal_types = patches_eye(1).multi_modal_types;
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%%
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root_dir = 'C:\Users\Tadas\Dropbox\AAM\test data\gaze_original\p00/';
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images = dir([root_dir, '*.jpg']);
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% root_dir = 'C:\Users\Tadas\Dropbox\AAM\test data\gaze_original\p00/';
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% images = dir([root_dir, '*.jpg']);
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root_dir = './sample_eye_imgs/';
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images = dir([root_dir, '/*.png']);
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verbose = true;
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@ -83,43 +91,36 @@ for img=1:numel(images)
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% shape correction for matlab format
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shape = shape + 1;
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% Perform eye fitting now
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shape_r_eye = zeros(numel(pdm_right_eye.M)/3, 2);
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shape_r_eye(right_eye_inds_synth,:) = shape(right_eye_inds, :);
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if(verbose)
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[ a, R, T, ~, l_params] = fit_PDM_ortho_proj_to_2D(pdm_right_eye.M, pdm_right_eye.E, pdm_right_eye.V, shape_r_eye);
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% valid points to draw (not to draw self-occluded ones)
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v_points = logical(patches(1).visibilities(view_used,:));
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bbox = [min(shape_r_eye(:,1)), min(shape_r_eye(:,2)), max(shape_r_eye(:,1)), max(shape_r_eye(:,2))];
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try
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plot(shape(v_points,1), shape(v_points',2),'.r','MarkerSize',20);
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plot(shape(v_points,1), shape(v_points',2),'.b','MarkerSize',10);
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catch warn
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end
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end
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% Map from detected landmarks to eye params
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shape_r_eye = zeros(20,2);
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shape_r_eye([9,11,13,15,17,19],:) = shape([43,44,45,46,47,48], :);
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[ a, R, T, ~, params, err, shapeOrtho] = fit_PDM_ortho_proj_to_2D(pdm_eye.M, pdm_eye.E, pdm_eye.V, shape_r_eye);
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g_param = [a; Rot2Euler(R)'; T];
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l_param = params;
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% Use the initial global and local params for clm fitting in the image
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patches_eye(1).visibilities(1:8) = 0;
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patches_eye(2).visibilities(1:8) = 0;
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patches_eye(3).visibilities(1:8) = 0;
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[shape_eye,~,~,lhood,lmark_lhood,view_used] = Fitting_from_bb(image, [], bbox, pdm_eye, patches_eye, clmParams_eye, 'gparam', g_param, 'lparam', l_param);
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[shape_r_eye] = Fitting_from_bb(image, [], bbox, pdm_right_eye, patches_right_eye, clmParams_eye, 'gparam', g_param, 'lparam', l_params);
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plot(shape_eye(:,1), shape_eye(:,2), '.g', 'MarkerSize',15);
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% % Now do the eyes
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% min_x = shape(43,1);
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% max_x = shape(43,1);
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% bbox_eye = shape(43,1)
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% Perform eye fitting now
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shape_l_eye = zeros(numel(pdm_right_eye.M)/3, 2);
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shape_l_eye(left_eye_inds_synth,:) = shape(left_eye_inds, :);
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[ a, R, T, ~, l_params] = fit_PDM_ortho_proj_to_2D(pdm_left_eye.M, pdm_left_eye.E, pdm_left_eye.V, shape_l_eye);
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bbox = [min(shape_l_eye(:,1)), min(shape_l_eye(:,2)), max(shape_l_eye(:,1)), max(shape_l_eye(:,2))];
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g_param = [a; Rot2Euler(R)'; T];
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[shape_l_eye] = Fitting_from_bb(image, [], bbox, pdm_left_eye, patches_left_eye, clmParams_eye, 'gparam', g_param, 'lparam', l_params);
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plot(shape_l_eye(9:20,1), shape_l_eye(9:20,2), '.g', 'MarkerSize',15);
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plot(shape_l_eye(1:8,1), shape_l_eye(1:8,2), '.b', 'MarkerSize',15);
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plot(shape_r_eye(9:20,1), shape_r_eye(9:20,2), '.g', 'MarkerSize',15);
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plot(shape_r_eye(1:8,1), shape_r_eye(1:8,2), '.b', 'MarkerSize',15);
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end
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hold off;
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@ -1,80 +0,0 @@
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function Script_PDM_eyes()
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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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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/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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%% 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_eyes.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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num_points = numel(M)/3;
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errors = zeros(numel(images),1);
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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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errors_normed = zeros(numel(images),1);
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errors_left_eye = zeros(numel(images),1);
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errors_right_eye = zeros(numel(images),1);
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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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labels_curr = squeeze(labels(i,:,:));
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[ a, R, T, ~, l_params, err, shapeOrtho] = fit_PDM_ortho_proj_to_2D(pdm.M, pdm.E, pdm.V, labels_curr);
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shape = shapeOrtho;
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shapes_all(:,:,i) = shapeOrtho;
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labels_all(:,:,i) = labels_curr;
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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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valid_points = sum(squeeze(labels(i,:,:)),2) > 0;
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valid_points(1:17) = 0;
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actualShape = squeeze(labels(i,:,:));
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errors(i) = sqrt(mean(sum((actualShape(valid_points,:) - shape(valid_points,:)).^2,2)));
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width = ((max(actualShape(valid_points,1)) - min(actualShape(valid_points,1)))+(max(actualShape(valid_points,2)) - min(actualShape(valid_points,2))))/2;
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errors_normed(i) = errors(i)/width;
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errors_left_eye(i) = compute_error_point_to_line_left_eye(actualShape, shapeOrtho, [0]);
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errors_right_eye(i) = compute_error_point_to_line_right_eye(actualShape, shapeOrtho, [0]);
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if(errors_normed(i) > 0.035 || errors_left_eye(i) > 0.035 || errors_right_eye(i) > 0.035)
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imshow(image);hold on; plot(shape(:,1), shape(:,2), '.g'); hold off;
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end
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end
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save('Errors_PDM_eyes.mat', 'errors_normed', 'errors_left_eye', 'errors_right_eye');
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end
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@ -1,80 +0,0 @@
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function Script_PDM_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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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/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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%% 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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num_points = numel(M)/3;
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errors = zeros(numel(images),1);
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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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errors_normed = zeros(numel(images),1);
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errors_left_eye = zeros(numel(images),1);
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errors_right_eye = zeros(numel(images),1);
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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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labels_curr = squeeze(labels(i,:,:));
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[ a, R, T, ~, l_params, err, shapeOrtho] = fit_PDM_ortho_proj_to_2D(pdm.M, pdm.E, pdm.V, labels_curr);
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shape = shapeOrtho;
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shapes_all(:,:,i) = shapeOrtho;
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labels_all(:,:,i) = labels_curr;
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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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valid_points = sum(squeeze(labels(i,:,:)),2) > 0;
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valid_points(1:17) = 0;
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actualShape = squeeze(labels(i,:,:));
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errors(i) = sqrt(mean(sum((actualShape(valid_points,:) - shape(valid_points,:)).^2,2)));
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width = ((max(actualShape(valid_points,1)) - min(actualShape(valid_points,1)))+(max(actualShape(valid_points,2)) - min(actualShape(valid_points,2))))/2;
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errors_normed(i) = errors(i)/width;
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errors_left_eye(i) = compute_error_point_to_line_left_eye(actualShape, shapeOrtho, [0]);
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errors_right_eye(i) = compute_error_point_to_line_right_eye(actualShape, shapeOrtho, [0]);
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if(errors_normed(i) > 0.035 || errors_left_eye(i) > 0.035 || errors_right_eye(i) > 0.035)
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imshow(image);hold on; plot(shape(:,1), shape(:,2), '.g'); hold off;
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end
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end
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save('Errors_PDM_basic.mat', 'errors_normed', 'errors_left_eye', 'errors_right_eye');
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end
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@ -1,86 +0,0 @@
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function [ error_per_image ] = compute_error_point_to_line_left_eye( ground_truth_all, detected_points_all, occluded )
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%compute_error
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% compute the average point-to-point Euclidean error normalized by the
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% inter-ocular distance (measured as the Euclidean distance between the
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% outer corners of the eyes)
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%
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% Inputs:
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% grounth_truth_all, size: num_of_points x 2 x num_of_images
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% detected_points_all, size: num_of_points x 2 x num_of_images
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% Output:
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% error_per_image, size: num_of_images x 1
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right_eye_inds_from_68 = [37,38,39,40,41,42,37];
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right_eye_inds_from_28 = [9,11,13,15,17,19];
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num_of_images = size(ground_truth_all,3);
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num_points_gt = size(ground_truth_all,1);
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num_points_det = size(detected_points_all,1);
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error_per_image = zeros(num_of_images,1);
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for i =1:num_of_images
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if(num_points_det == 6)
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detected_points = detected_points_all(:,:,i);
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elseif(num_points_det == 68 || num_points_det == 66)
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detected_points = detected_points_all(right_eye_inds_from_68,:,i);
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elseif(num_points_det == 28)
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detected_points = detected_points_all(right_eye_inds_from_28,:,i);
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elseif(num_points_det == 49)
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end
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ground_truth_points = ground_truth_all(:,:,i);
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if(num_points_gt == 66 || num_points_gt == 68)
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interocular_distance = norm(ground_truth_points(37,:)-ground_truth_points(46,:));
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ground_truth_points = ground_truth_points(right_eye_inds_from_68,:,:);
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else
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interocular_distance = norm(ground_truth_points(37-17,:)-ground_truth_points(46-17,:));
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ground_truth_points = ground_truth_points(right_eye_inds_from_68,:,:);
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end
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sum=0;
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for j=1:6
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if(j== 1 || j == 6)
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% eye corners should align perfectly
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sum = sum + norm(detected_points(j,:)-ground_truth_points(j,:));
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else
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% points between eye corners measured in distance to the two appropriate line
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% segments
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sum = sum + point_to_segments(detected_points(j,:), ground_truth_points(j-1:j+1,:));
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end
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end
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error_per_image(i) = sum/(6*interocular_distance);
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end
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error_per_image = error_per_image(~occluded);
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end
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function seg_dist = point_to_segments(point, segments)
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seg_dists = zeros(size(segments, 1)-1,1);
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for i=1:size(segments, 1)-1
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vec1 = point - segments(i,:);
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vec2 = segments(i+1,:) - segments(i,:);
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d = (vec1 * vec2') / (norm(vec2)^2);
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if(d < 0)
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seg_dists(i) = norm(vec1);
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elseif(d > 1)
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seg_dists(i) = norm(point - segments(i+1,:));
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else
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seg_dists(i) = sqrt( norm(vec1)^2 - norm(d * vec2)^2);
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end
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end
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seg_dist = min(seg_dists);
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end
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@ -1,86 +0,0 @@
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function [ error_per_image ] = compute_error_point_to_line_right_eye( ground_truth_all, detected_points_all, occluded )
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%compute_error
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% compute the average point-to-point Euclidean error normalized by the
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% inter-ocular distance (measured as the Euclidean distance between the
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% outer corners of the eyes)
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%
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% Inputs:
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% grounth_truth_all, size: num_of_points x 2 x num_of_images
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% detected_points_all, size: num_of_points x 2 x num_of_images
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% Output:
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% error_per_image, size: num_of_images x 1
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right_eye_inds_from_68 = [43,44,45,46,47,48,43];
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right_eye_inds_from_28 = [9,11,13,15,17,19];
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num_of_images = size(ground_truth_all,3);
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num_points_gt = size(ground_truth_all,1);
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num_points_det = size(detected_points_all,1);
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error_per_image = zeros(num_of_images,1);
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for i =1:num_of_images
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if(num_points_det == 6)
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detected_points = detected_points_all(:,:,i);
|
||||
elseif(num_points_det == 68 || num_points_det == 66)
|
||||
detected_points = detected_points_all(right_eye_inds_from_68,:,i);
|
||||
elseif(num_points_det == 28)
|
||||
detected_points = detected_points_all(right_eye_inds_from_28,:,i);
|
||||
elseif(num_points_det == 49)
|
||||
|
||||
end
|
||||
ground_truth_points = ground_truth_all(:,:,i);
|
||||
|
||||
if(num_points_gt == 66 || num_points_gt == 68)
|
||||
interocular_distance = norm(ground_truth_points(37,:)-ground_truth_points(46,:));
|
||||
ground_truth_points = ground_truth_points(right_eye_inds_from_68,:,:);
|
||||
else
|
||||
interocular_distance = norm(ground_truth_points(37-17,:)-ground_truth_points(46-17,:));
|
||||
ground_truth_points = ground_truth_points(right_eye_inds_from_68,:,:);
|
||||
end
|
||||
|
||||
sum=0;
|
||||
for j=1:6
|
||||
|
||||
if(j== 1 || j == 6)
|
||||
% eye corners should align perfectly
|
||||
sum = sum + norm(detected_points(j,:)-ground_truth_points(j,:));
|
||||
else
|
||||
% points between eye corners measured in distance to the two appropriate line
|
||||
% segments
|
||||
sum = sum + point_to_segments(detected_points(j,:), ground_truth_points(j-1:j+1,:));
|
||||
end
|
||||
end
|
||||
error_per_image(i) = sum/(6*interocular_distance);
|
||||
end
|
||||
|
||||
error_per_image = error_per_image(~occluded);
|
||||
|
||||
end
|
||||
|
||||
function seg_dist = point_to_segments(point, segments)
|
||||
|
||||
seg_dists = zeros(size(segments, 1)-1,1);
|
||||
|
||||
for i=1:size(segments, 1)-1
|
||||
|
||||
vec1 = point - segments(i,:);
|
||||
vec2 = segments(i+1,:) - segments(i,:);
|
||||
|
||||
d = (vec1 * vec2') / (norm(vec2)^2);
|
||||
|
||||
if(d < 0)
|
||||
seg_dists(i) = norm(vec1);
|
||||
elseif(d > 1)
|
||||
seg_dists(i) = norm(point - segments(i+1,:));
|
||||
else
|
||||
seg_dists(i) = sqrt( norm(vec1)^2 - norm(d * vec2)^2);
|
||||
end
|
||||
end
|
||||
seg_dist = min(seg_dists);
|
||||
end
|
||||
|
|
@ -27,8 +27,12 @@ pdm.M = double(M);
|
|||
pdm.E = double(E);
|
||||
pdm.V = double(V);
|
||||
|
||||
num_points = numel(M)/3;
|
||||
|
||||
errors = zeros(numel(images),1);
|
||||
shapes_all = zeros(size(labels,2),size(labels,3), size(labels,1));
|
||||
labels_all = zeros(size(labels,2),size(labels,3), size(labels,1));
|
||||
errors_normed = zeros(numel(images),1);
|
||||
|
||||
errors_left_eye = zeros(numel(images),1);
|
||||
errors_right_eye = zeros(numel(images),1);
|
||||
|
|
@ -36,30 +40,41 @@ errors_right_eye = zeros(numel(images),1);
|
|||
tic
|
||||
for i=1:numel(images)
|
||||
|
||||
image = imread(images(i).img);
|
||||
image_orig = image;
|
||||
|
||||
if(size(image,3) == 3)
|
||||
image = rgb2gray(image);
|
||||
end
|
||||
|
||||
labels_curr = squeeze(labels(i,:,:));
|
||||
|
||||
[ a, R, T, ~, l_params, err, shapeOrtho] = fit_PDM_ortho_proj_to_2D_no_reg(pdm.M, pdm.E, pdm.V, labels_curr);
|
||||
[ a, R, T, ~, l_params, err, shapeOrtho] = fit_PDM_ortho_proj_to_2D(pdm.M, pdm.E, pdm.V, labels_curr);
|
||||
|
||||
shape = shapeOrtho;
|
||||
shapes_all(:,:,i) = shapeOrtho;
|
||||
labels_all(:,:,i) = labels_curr;
|
||||
|
||||
if(mod(i, 100)==0)
|
||||
if(mod(i, 200)==0)
|
||||
fprintf('%d done\n', i );
|
||||
end
|
||||
|
||||
valid_points = sum(squeeze(labels(i,:,:)),2) > 0;
|
||||
valid_points(1:17) = 0;
|
||||
|
||||
actualShape = squeeze(labels(i,:,:));
|
||||
errors(i) = sqrt(mean(sum((actualShape(valid_points,:) - shape(valid_points,:)).^2,2)));
|
||||
width = ((max(actualShape(valid_points,1)) - min(actualShape(valid_points,1)))+(max(actualShape(valid_points,2)) - min(actualShape(valid_points,2))))/2;
|
||||
errors_normed(i) = errors(i)/width;
|
||||
|
||||
errors_left_eye(i) = compute_error_point_to_line_left_eye(actualShape, shapeOrtho, [0]);
|
||||
errors_right_eye(i) = compute_error_point_to_line_right_eye(actualShape, shapeOrtho, [0]);
|
||||
|
||||
if(errors_left_eye(i) > 0.02 || errors_right_eye(i) > 0.02)
|
||||
plot(shapeOrtho(:,1), -shapeOrtho(:,2), 'r.'); hold on;
|
||||
axis equal;
|
||||
plot(labels_curr(:,1), -labels_curr(:,2), 'g.'); hold off;
|
||||
if(errors_normed(i) > 0.035 || errors_left_eye(i) > 0.035 || errors_right_eye(i) > 0.035)
|
||||
imshow(image);hold on; plot(shape(:,1), shape(:,2), '.g'); hold off;
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
save('Errors_PDM_eyes.mat', 'errors_left_eye', 'errors_right_eye');
|
||||
save('Errors_PDM_eyes.mat', 'errors_normed', 'errors_left_eye', 'errors_right_eye');
|
||||
|
||||
end
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
function Script_PDM_eyes()
|
||||
function Script_PDM_general()
|
||||
|
||||
addpath('../PDM_helpers/');
|
||||
addpath('../fitting/normxcorr2_mex_ALL');
|
||||
|
|
@ -27,8 +27,12 @@ pdm.M = double(M);
|
|||
pdm.E = double(E);
|
||||
pdm.V = double(V);
|
||||
|
||||
num_points = numel(M)/3;
|
||||
|
||||
errors = zeros(numel(images),1);
|
||||
shapes_all = zeros(size(labels,2),size(labels,3), size(labels,1));
|
||||
labels_all = zeros(size(labels,2),size(labels,3), size(labels,1));
|
||||
errors_normed = zeros(numel(images),1);
|
||||
|
||||
errors_left_eye = zeros(numel(images),1);
|
||||
errors_right_eye = zeros(numel(images),1);
|
||||
|
|
@ -36,31 +40,41 @@ errors_right_eye = zeros(numel(images),1);
|
|||
tic
|
||||
for i=1:numel(images)
|
||||
|
||||
image = imread(images(i).img);
|
||||
image_orig = image;
|
||||
|
||||
if(size(image,3) == 3)
|
||||
image = rgb2gray(image);
|
||||
end
|
||||
|
||||
labels_curr = squeeze(labels(i,:,:));
|
||||
|
||||
[ a, R, T, ~, l_params, err, shapeOrtho] = fit_PDM_ortho_proj_to_2D_no_reg(pdm.M, pdm.E, pdm.V, labels_curr);
|
||||
[ a, R, T, ~, l_params, err, shapeOrtho] = fit_PDM_ortho_proj_to_2D(pdm.M, pdm.E, pdm.V, labels_curr);
|
||||
|
||||
shape = shapeOrtho;
|
||||
shapes_all(:,:,i) = shapeOrtho;
|
||||
labels_all(:,:,i) = labels_curr;
|
||||
|
||||
if(mod(i, 100)==0)
|
||||
if(mod(i, 200)==0)
|
||||
fprintf('%d done\n', i );
|
||||
end
|
||||
|
||||
valid_points = sum(squeeze(labels(i,:,:)),2) > 0;
|
||||
valid_points(1:17) = 0;
|
||||
|
||||
actualShape = squeeze(labels(i,:,:));
|
||||
errors(i) = sqrt(mean(sum((actualShape(valid_points,:) - shape(valid_points,:)).^2,2)));
|
||||
width = ((max(actualShape(valid_points,1)) - min(actualShape(valid_points,1)))+(max(actualShape(valid_points,2)) - min(actualShape(valid_points,2))))/2;
|
||||
errors_normed(i) = errors(i)/width;
|
||||
|
||||
errors_left_eye(i) = compute_error_point_to_line_left_eye(actualShape, shapeOrtho, [0]);
|
||||
errors_right_eye(i) = compute_error_point_to_line_right_eye(actualShape, shapeOrtho, [0]);
|
||||
|
||||
|
||||
if(errors_left_eye(i) > 0.02 || errors_right_eye(i) > 0.02)
|
||||
plot(shapeOrtho(:,1), -shapeOrtho(:,2), 'r.'); hold on;
|
||||
axis equal;
|
||||
plot(labels_curr(:,1), -labels_curr(:,2), 'g.'); hold off;
|
||||
if(errors_normed(i) > 0.035 || errors_left_eye(i) > 0.035 || errors_right_eye(i) > 0.035)
|
||||
imshow(image);hold on; plot(shape(:,1), shape(:,2), '.g'); hold off;
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
save('Errors_PDM_basic.mat', 'errors_left_eye', 'errors_right_eye');
|
||||
save('Errors_PDM_basic.mat', 'errors_normed', 'errors_left_eye', 'errors_right_eye');
|
||||
|
||||
end
|
||||
|
|
|
|||
|
|
@ -59,9 +59,7 @@ for i =1:num_of_images
|
|||
error_per_image(i) = sum/(6*interocular_distance);
|
||||
end
|
||||
|
||||
if(nargin > 2)
|
||||
error_per_image = error_per_image(~occluded);
|
||||
end
|
||||
error_per_image = error_per_image(~occluded);
|
||||
|
||||
end
|
||||
|
||||
|
|
|
|||
|
|
@ -59,9 +59,7 @@ for i =1:num_of_images
|
|||
error_per_image(i) = sum/(6*interocular_distance);
|
||||
end
|
||||
|
||||
if(nargin > 2)
|
||||
error_per_image = error_per_image(~occluded);
|
||||
end
|
||||
error_per_image = error_per_image(~occluded);
|
||||
|
||||
end
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,38 @@
|
|||
function [ clmParams, pdm_right, pdm_left ] = Load_CLM_params_eye_28()
|
||||
%LOAD_CLM_PARAMS_WILD Summary of this function goes here
|
||||
% Detailed explanation goes here
|
||||
clmParams.window_size = [17,17; 15,15; 13,13;];
|
||||
clmParams.numPatchIters = size(clmParams.window_size,1);
|
||||
|
||||
% the PDM created from in the wild data
|
||||
pdmLoc = ['../models/hierarch_pdm/pdm_28_r_eye.mat'];
|
||||
|
||||
load(pdmLoc);
|
||||
|
||||
pdm_right = struct;
|
||||
pdm_right.M = double(M);
|
||||
pdm_right.E = double(E);
|
||||
pdm_right.V = double(V);
|
||||
|
||||
pdmLoc = ['../models/hierarch_pdm/pdm_28_l_eye.mat'];
|
||||
|
||||
load(pdmLoc);
|
||||
|
||||
pdm_left = struct;
|
||||
pdm_left.M = double(M);
|
||||
pdm_left.E = double(E);
|
||||
pdm_left.V = double(V);
|
||||
|
||||
% the default model parameters to use
|
||||
clmParams.regFactor = 2.0;
|
||||
clmParams.sigmaMeanShift = 1.5;
|
||||
clmParams.tikhonov_factor = 0;
|
||||
|
||||
clmParams.startScale = 1;
|
||||
clmParams.num_RLMS_iter = 10;
|
||||
clmParams.fTol = 0.01;
|
||||
clmParams.useMultiScale = true;
|
||||
clmParams.use_multi_modal = 0;
|
||||
clmParams.tikhonov_factor = 0;
|
||||
end
|
||||
|
||||
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Loading…
Reference in New Issue