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Fixing eye landmark Matlab demo.

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This commit is contained in:
Tadas Baltrusaitis 2016-06-13 20:48:06 -04:00
parent 4a73ece996
commit e0fa16ee04
25 changed files with 130 additions and 394 deletions
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4
.gitignore vendored
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@ -20,3 +20,7 @@ matlab_runners/Feature Point Experiments/yt_features/
matlab_runners/Feature Point Experiments/yt_features_clm/
matlab_runners/Gaze Experiments/mpii_out/
build/
Release/AU_predictors/
Release/
exe/Recording/recording/
ipch/

73
matlab_version/demo/face_image_demo_eyes.m
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@ -9,22 +9,30 @@ addpath('../CCNF/');
[clmParams, pdm] = Load_CLM_params_wild();
[clmParams_eye, pdm_eye] = Load_CLM_params_eye();
% An accurate CCNF (or CLNF) model
% [patches] = Load_Patch_Experts( '../models/general/', 'ccnf_patches_*_general.mat', [], [], clmParams);
% A simpler (but less accurate SVR)
[patches] = Load_Patch_Experts( '../models/general/', 'svr_patches_*_general.mat', [], [], clmParams);
[patches_eye] = Load_Patch_Experts( 'C:\Users\Tadas\Dropbox\AAM\patch_experts_eyes\svr_training\trained/', 'svr_patches_*_synth.mat', [], [], clmParams);
% Loading eye PDM and patch experts
[clmParams_eye, pdm_right_eye, pdm_left_eye] = Load_CLM_params_eye_28();
[patches_right_eye] = Load_Patch_Experts( '../models/hierarch/', 'ccnf_patches_*_synth_right_eye.mat', [], [], clmParams_eye);
[patches_left_eye] = Load_Patch_Experts( '../models/hierarch/', 'ccnf_patches_*_synth_left_eye.mat', [], [], clmParams_eye);
clmParams_eye.multi_modal_types = patches_right_eye(1).multi_modal_types;
right_eye_inds = [43,44,45,46,47,48];
left_eye_inds = [37,38,39,40,41,42];
right_eye_inds_synth = [9 11 13 15 17 19];
left_eye_inds_synth = [9 11 13 15 17 19];
clmParams.multi_modal_types = patches(1).multi_modal_types;
clmParams_eye.multi_modal_types = patches_eye(1).multi_modal_types;
%%
root_dir = 'C:\Users\Tadas\Dropbox\AAM\test data\gaze_original\p00/';
images = dir([root_dir, '*.jpg']);
% root_dir = 'C:\Users\Tadas\Dropbox\AAM\test data\gaze_original\p00/';
% images = dir([root_dir, '*.jpg']);
root_dir = './sample_eye_imgs/';
images = dir([root_dir, '/*.png']);
verbose = true;
@ -84,42 +92,35 @@ for img=1:numel(images)
% shape correction for matlab format
shape = shape + 1;
if(verbose)
% Perform eye fitting now
shape_r_eye = zeros(numel(pdm_right_eye.M)/3, 2);
shape_r_eye(right_eye_inds_synth,:) = shape(right_eye_inds, :);
% valid points to draw (not to draw self-occluded ones)
v_points = logical(patches(1).visibilities(view_used,:));
[ 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);
try
plot(shape(v_points,1), shape(v_points',2),'.r','MarkerSize',20);
plot(shape(v_points,1), shape(v_points',2),'.b','MarkerSize',10);
catch warn
end
end
% Map from detected landmarks to eye params
shape_r_eye = zeros(20,2);
shape_r_eye([9,11,13,15,17,19],:) = shape([43,44,45,46,47,48], :);
[ a, R, T, ~, params, err, shapeOrtho] = fit_PDM_ortho_proj_to_2D(pdm_eye.M, pdm_eye.E, pdm_eye.V, shape_r_eye);
bbox = [min(shape_r_eye(:,1)), min(shape_r_eye(:,2)), max(shape_r_eye(:,1)), max(shape_r_eye(:,2))];
g_param = [a; Rot2Euler(R)'; T];
l_param = params;
% Use the initial global and local params for clm fitting in the image
patches_eye(1).visibilities(1:8) = 0;
patches_eye(2).visibilities(1:8) = 0;
patches_eye(3).visibilities(1:8) = 0;
[shape_eye,~,~,lhood,lmark_lhood,view_used] = Fitting_from_bb(image, [], bbox, pdm_eye, patches_eye, clmParams_eye, 'gparam', g_param, 'lparam', l_param);
[shape_r_eye] = Fitting_from_bb(image, [], bbox, pdm_right_eye, patches_right_eye, clmParams_eye, 'gparam', g_param, 'lparam', l_params);
plot(shape_eye(:,1), shape_eye(:,2), '.g', 'MarkerSize',15);
% % Now do the eyes
% min_x = shape(43,1);
% max_x = shape(43,1);
% bbox_eye = shape(43,1)
% Perform eye fitting now
shape_l_eye = zeros(numel(pdm_right_eye.M)/3, 2);
shape_l_eye(left_eye_inds_synth,:) = shape(left_eye_inds, :);
[ 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);
bbox = [min(shape_l_eye(:,1)), min(shape_l_eye(:,2)), max(shape_l_eye(:,1)), max(shape_l_eye(:,2))];
g_param = [a; Rot2Euler(R)'; T];
[shape_l_eye] = Fitting_from_bb(image, [], bbox, pdm_left_eye, patches_left_eye, clmParams_eye, 'gparam', g_param, 'lparam', l_params);
plot(shape_l_eye(9:20,1), shape_l_eye(9:20,2), '.g', 'MarkerSize',15);
plot(shape_l_eye(1:8,1), shape_l_eye(1:8,2), '.b', 'MarkerSize',15);
plot(shape_r_eye(9:20,1), shape_r_eye(9:20,2), '.g', 'MarkerSize',15);
plot(shape_r_eye(1:8,1), shape_r_eye(1:8,2), '.b', 'MarkerSize',15);
end
hold off;

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80
matlab_version/experiments_in_the_wild/Script_PDM_eyes.m
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@ -1,80 +0,0 @@
function Script_PDM_eyes()
addpath('../PDM_helpers/');
addpath('../fitting/normxcorr2_mex_ALL');
addpath('../fitting/');
addpath('../CCNF/');
addpath('../models/');
% Replace this with the location of in 300 faces in the wild data
if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
root_test_data = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];
else
root_test_data = 'F:/Dropbox/Dropbox/AAM/test data/';
end
[images, detections, labels] = Collect_wild_imgs(root_test_data);
%% Fitting the model to the provided image
% the default PDM to use
pdmLoc = ['../models/pdm/pdm_68_aligned_wild_eyes.mat'];
load(pdmLoc);
pdm = struct;
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);
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(pdm.M, pdm.E, pdm.V, labels_curr);
shape = shapeOrtho;
shapes_all(:,:,i) = shapeOrtho;
labels_all(:,:,i) = labels_curr;
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_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_normed', 'errors_left_eye', 'errors_right_eye');
end

80
matlab_version/experiments_in_the_wild/Script_PDM_general.m
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@ -1,80 +0,0 @@
function Script_PDM_general()
addpath('../PDM_helpers/');
addpath('../fitting/normxcorr2_mex_ALL');
addpath('../fitting/');
addpath('../CCNF/');
addpath('../models/');
% Replace this with the location of in 300 faces in the wild data
if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
root_test_data = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];
else
root_test_data = 'F:/Dropbox/Dropbox/AAM/test data/';
end
[images, detections, labels] = Collect_wild_imgs(root_test_data);
%% Fitting the model to the provided image
% the default PDM to use
pdmLoc = ['../models/pdm/pdm_68_aligned_wild.mat'];
load(pdmLoc);
pdm = struct;
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);
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(pdm.M, pdm.E, pdm.V, labels_curr);
shape = shapeOrtho;
shapes_all(:,:,i) = shapeOrtho;
labels_all(:,:,i) = labels_curr;
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_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_normed', 'errors_left_eye', 'errors_right_eye');
end

86
matlab_version/experiments_in_the_wild/compute_error_point_to_line_left_eye.m
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@ -1,86 +0,0 @@
function [ error_per_image ] = compute_error_point_to_line_left_eye( ground_truth_all, detected_points_all, occluded )
%compute_error
% compute the average point-to-point Euclidean error normalized by the
% inter-ocular distance (measured as the Euclidean distance between the
% outer corners of the eyes)
%
% Inputs:
% grounth_truth_all, size: num_of_points x 2 x num_of_images
% detected_points_all, size: num_of_points x 2 x num_of_images
% Output:
% error_per_image, size: num_of_images x 1
right_eye_inds_from_68 = [37,38,39,40,41,42,37];
right_eye_inds_from_28 = [9,11,13,15,17,19];
num_of_images = size(ground_truth_all,3);
num_points_gt = size(ground_truth_all,1);
num_points_det = size(detected_points_all,1);
error_per_image = zeros(num_of_images,1);
for i =1:num_of_images
if(num_points_det == 6)
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

86
matlab_version/experiments_in_the_wild/compute_error_point_to_line_right_eye.m
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@ -1,86 +0,0 @@
function [ error_per_image ] = compute_error_point_to_line_right_eye( ground_truth_all, detected_points_all, occluded )
%compute_error
% compute the average point-to-point Euclidean error normalized by the
% inter-ocular distance (measured as the Euclidean distance between the
% outer corners of the eyes)
%
% Inputs:
% grounth_truth_all, size: num_of_points x 2 x num_of_images
% detected_points_all, size: num_of_points x 2 x num_of_images
% Output:
% error_per_image, size: num_of_images x 1
right_eye_inds_from_68 = [43,44,45,46,47,48,43];
right_eye_inds_from_28 = [9,11,13,15,17,19];
num_of_images = size(ground_truth_all,3);
num_points_gt = size(ground_truth_all,1);
num_points_det = size(detected_points_all,1);
error_per_image = zeros(num_of_images,1);
for i =1:num_of_images
if(num_points_det == 6)
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

0
matlab_version/experiments_in_the_wild/Script_CLNF_general_eyes.m → matlab_version/experiments_in_the_wild/hierarch_checks/Script_CLNF_general_eyes.m
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31
matlab_version/experiments_in_the_wild/hierarch_checks/Script_PDM_eyes.m
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@ -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

34
matlab_version/experiments_in_the_wild/hierarch_checks/Script_PDM_general.m
View file

@ -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

0
matlab_version/experiments_in_the_wild/compute_brow_error_to_line.m → matlab_version/experiments_in_the_wild/hierarch_checks/compute_brow_error_to_line.m
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4
matlab_version/experiments_in_the_wild/hierarch_checks/compute_error_point_to_line_left_eye.m
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@ -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

4
matlab_version/experiments_in_the_wild/hierarch_checks/compute_error_point_to_line_right_eye.m
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@ -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

38
matlab_version/models/Load_CLM_params_eye_28.m Normal file
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@ -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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