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Work towards cleaning up and implementing DCLM in C++

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This commit is contained in:
Tadas Baltrusaitis
2016-12-06 09:51:36 -05:00
parent 778d6dce44
commit 91890afd5b
67 changed files with 3392 additions and 220 deletions
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11
.gitignore vendored
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@@ -40,3 +40,14 @@ matlab_version/experiments_JANUS/wild_fit_clnf/
matlab_version/experiments_JANUS/wild_fit_dclm/
exe/FeatureExtraction/300VW_features_experiments/
matlab_runners/Feature Point Experiments/300VW_features_experiments/
matlab_version/experiments_300VW/CLM_res/
matlab_version/experiments_300VW/CLNF_res/
matlab_version/experiments_300VW/DCLM_res/
matlab_version/experiments_in_the_wild/patch_expert_responses/
matlab_version/experiments_in_the_wild/wild_fit_clnf/
matlab_version/experiments_in_the_wild/wild_fit_dclm/
matlab_version/experiments_iccv_300w/wild_fit_cfss/
matlab_version/experiments_iccv_300w/wild_fit_clnf/
matlab_version/experiments_iccv_300w/wild_fit_dclm/
matlab_version/experiments_JANUS/all_fit_cvpr/
matlab_version/experiments_JANUS/cfss_fit/

157
matlab_version/experiments_300VW/CLM_300VW.m Normal file
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clear;
addpath('../PDM_helpers/');
addpath('../fitting/normxcorr2_mex_ALL');
addpath('../fitting/');
addpath('../CCNF/');
addpath('../models/');
output_dir = './CLM_res/';
%% select database and load bb initializations
db_root = 'D:\Datasets\300VW_Dataset_2015_12_14\300VW_Dataset_2015_12_14/';
bb_root = '../..//matlab_runners/Feature Point Experiments/300VW_face_dets/';
extra_dir = 'D:\Datasets\300VW_Dataset_2015_12_14\extra';
in_dirs = dir(db_root);
in_dirs = in_dirs(3:end);
in_dirs = in_dirs(1:end-2);
preds_all = [];
gts_all = [];
%% loading the patch experts and the PDM
clmParams = struct;
clmParams.window_size = [25,25; 23,23; 21,21; 19,19; 17,17;];
clmParams.numPatchIters = size(clmParams.window_size,1);
[patches] = Load_Patch_Experts( '../models/general/', 'svr_patches_*_general.mat', [], [], clmParams);
% 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(pdm.M) / 3;
clmParams.regFactor = [35, 27, 20, 20];
clmParams.sigmaMeanShift = [1.25, 1.375, 1.5, 1.5];
clmParams.tikhonov_factor = [0,0,0, 0];
clmParams.startScale = 1;
clmParams.num_RLMS_iter = 10;
clmParams.fTol = 0.01;
clmParams.useMultiScale = true;
clmParams.use_multi_modal = 1;
clmParams.multi_modal_types = patches(1).multi_modal_types;
clmParams.numPatchIters = 4;
multi_view = true;
verbose = true;
%% Select video
for i=1:numel(in_dirs)
in_file_name = [db_root '/', in_dirs(i).name, '/vid.avi'];
vid = VideoReader(in_file_name);
bounding_boxes = dlmread([bb_root, in_dirs(i).name, '_dets.txt'], ',');
preds = [];
n_frames = size(bounding_boxes,1);
for f=1:n_frames
input_image = readFrame(vid);
reset = true;
%% Initialize from detected bounding boxes every 30 frames
if (mod(f-1, 30) == 0)
ind = min(f, size(bounding_boxes,1));
bb = bounding_boxes(ind, :);
% If no face detected use the closest detected BB
if(bb(3) == 0)
ind_next = ind + find(bounding_boxes(ind+1:end,3), 1);
if(isempty(bounding_boxes(ind_next)) || bounding_boxes(ind_next,3)==0)
ind_next = find(bounding_boxes(1:ind,3), 1, 'last');
end
bb = bounding_boxes(ind_next, :);
end
bb(3) = bb(1) + bb(3);
bb(4) = bb(2) + bb(4);
reset = true;
else
reset = false;
end
% have a multi-view version for initialization, otherwise use
% previous shape
if(reset && multi_view)
clmParams.window_size = [25,25; 23,23; 21,21; 21,21];
clmParams.numPatchIters = 4;
clmParams.startScale = 1;
views = [0,0,0; 0,-45,0; -30,0,0; 0,45,0; 30,0,0];
views = views * pi/180;
shapes = zeros(num_points, 2, size(views,1));
ls = zeros(size(views,1),1);
lmark_lhoods = zeros(num_points,size(views,1));
views_used = zeros(num_points,size(views,1));
% Find the best orientation
for v = 1:size(views,1)
[shapes(:,:,v),g_param,l_param,ls(v),lmark_lhoods(:,v),views_used(v)] = Fitting_from_bb(input_image, [], bb, pdm, patches, clmParams, 'orientation', views(v,:));
end
[lhood, v_ind] = max(ls);
lmark_lhood = lmark_lhoods(:,v_ind);
shape = shapes(:,:,v_ind);
view_used = views_used(v);
else
clmParams.window_size = [23,23; 21,21; 19,19; 17,17];
clmParams.numPatchIters = 3;
clmParams.startScale = 2;
[shape,g_param,l_param,lhood,lmark_lhood,view_used] = Fitting_from_bb(input_image, [], bb, pdm, patches, clmParams, 'gparam', g_param, 'lparam', l_param);
end
preds = cat(3, preds, shape);
%% plot the result
imshow(input_image);
hold on;
plot(shape(:,1), shape(:,2), '.r');
% rectangle('Position', [bb(2), bb(1), bb(4), bb(3)]);
hold off;
drawnow expose
end
%% Grab the ground truth
fps_all = dir([db_root, '/', in_dirs(i).name, '/annot/*.pts']);
gt_landmarks = zeros([68, 2, size(fps_all)]);
for k = 1:size(fps_all)
gt_landmarks_frame = dlmread([db_root, '/', in_dirs(i).name, '/annot/', fps_all(k).name], ' ', 'A4..B71');
gt_landmarks(:,:,k) = gt_landmarks_frame;
end
if(size(gt_landmarks,3) ~= size(preds,3))
fprintf('something went wrong with vid %d\n', i);
end
% Remove unreliable frames
if(exist([extra_dir, '/', in_dirs(i).name, '.mat'], 'file'))
load([extra_dir, '/', in_dirs(i).name, '.mat']);
gt_landmarks(:,:,int32(error)) = [];
preds(:,:,int32(error))=[];
end
vid_name = in_dirs(i).name;
save([output_dir, '/', vid_name], 'preds', 'gt_landmarks');
end
% [pocr_error, err_pp_pocr] = compute_error( gts_all, preds_all);

157
matlab_version/experiments_300VW/CLNF_300VW.m Normal file
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@@ -0,0 +1,157 @@
clear;
addpath('../PDM_helpers/');
addpath('../fitting/normxcorr2_mex_ALL');
addpath('../fitting/');
addpath('../CCNF/');
addpath('../models/');
output_dir = './CLNF_res/';
%% select database and load bb initializations
db_root = 'D:\Datasets\300VW_Dataset_2015_12_14\300VW_Dataset_2015_12_14/';
bb_root = '../..//matlab_runners/Feature Point Experiments/300VW_face_dets/';
extra_dir = 'D:\Datasets\300VW_Dataset_2015_12_14\extra';
in_dirs = dir(db_root);
in_dirs = in_dirs(3:end);
in_dirs = in_dirs(1:end-2);
preds_all = [];
gts_all = [];
%% loading the patch experts and the PDM
clmParams = struct;
clmParams.window_size = [25,25; 23,23; 21,21; 19,19; 17,17;];
clmParams.numPatchIters = size(clmParams.window_size,1);
[patches] = Load_Patch_Experts( '../models/general/', 'ccnf_patches_*_general.mat', [], [], clmParams);
% 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(pdm.M) / 3;
clmParams.regFactor = [35, 27, 20, 20];
clmParams.sigmaMeanShift = [1.25, 1.375, 1.5, 1.5];
clmParams.tikhonov_factor = [0,0,0, 0];
clmParams.startScale = 1;
clmParams.num_RLMS_iter = 10;
clmParams.fTol = 0.01;
clmParams.useMultiScale = true;
clmParams.use_multi_modal = 1;
clmParams.multi_modal_types = patches(1).multi_modal_types;
clmParams.numPatchIters = 4;
multi_view = true;
verbose = true;
%% Select video
for i=1:numel(in_dirs)
in_file_name = [db_root '/', in_dirs(i).name, '/vid.avi'];
vid = VideoReader(in_file_name);
bounding_boxes = dlmread([bb_root, in_dirs(i).name, '_dets.txt'], ',');
preds = [];
n_frames = size(bounding_boxes,1);
for f=1:n_frames
input_image = readFrame(vid);
reset = true;
%% Initialize from detected bounding boxes every 30 frames
if (mod(f-1, 30) == 0)
ind = min(f, size(bounding_boxes,1));
bb = bounding_boxes(ind, :);
% If no face detected use the closest detected BB
if(bb(3) == 0)
ind_next = ind + find(bounding_boxes(ind+1:end,3), 1);
if(isempty(bounding_boxes(ind_next)) || bounding_boxes(ind_next,3)==0)
ind_next = find(bounding_boxes(1:ind,3), 1, 'last');
end
bb = bounding_boxes(ind_next, :);
end
bb(3) = bb(1) + bb(3);
bb(4) = bb(2) + bb(4);
reset = true;
else
reset = false;
end
% have a multi-view version for initialization, otherwise use
% previous shape
if(reset && multi_view)
clmParams.window_size = [25,25; 23,23; 21,21; 21,21];
clmParams.numPatchIters = 4;
clmParams.startScale = 1;
views = [0,0,0; 0,-45,0; -30,0,0; 0,45,0; 30,0,0];
views = views * pi/180;
shapes = zeros(num_points, 2, size(views,1));
ls = zeros(size(views,1),1);
lmark_lhoods = zeros(num_points,size(views,1));
views_used = zeros(num_points,size(views,1));
% Find the best orientation
for v = 1:size(views,1)
[shapes(:,:,v),g_param,l_param,ls(v),lmark_lhoods(:,v),views_used(v)] = Fitting_from_bb(input_image, [], bb, pdm, patches, clmParams, 'orientation', views(v,:));
end
[lhood, v_ind] = max(ls);
lmark_lhood = lmark_lhoods(:,v_ind);
shape = shapes(:,:,v_ind);
view_used = views_used(v);
else
clmParams.window_size = [23,23; 21,21; 19,19; 17,17];
clmParams.numPatchIters = 3;
clmParams.startScale = 2;
[shape,g_param,l_param,lhood,lmark_lhood,view_used] = Fitting_from_bb(input_image, [], bb, pdm, patches, clmParams, 'gparam', g_param, 'lparam', l_param);
end
preds = cat(3, preds, shape);
%% plot the result
imshow(input_image);
hold on;
plot(shape(:,1), shape(:,2), '.r');
% rectangle('Position', [bb(2), bb(1), bb(4), bb(3)]);
hold off;
drawnow expose
end
%% Grab the ground truth
fps_all = dir([db_root, '/', in_dirs(i).name, '/annot/*.pts']);
gt_landmarks = zeros([68, 2, size(fps_all)]);
for k = 1:size(fps_all)
gt_landmarks_frame = dlmread([db_root, '/', in_dirs(i).name, '/annot/', fps_all(k).name], ' ', 'A4..B71');
gt_landmarks(:,:,k) = gt_landmarks_frame;
end
if(size(gt_landmarks,3) ~= size(preds,3))
fprintf('something went wrong with vid %d\n', i);
end
% Remove unreliable frames
if(exist([extra_dir, '/', in_dirs(i).name, '.mat'], 'file'))
load([extra_dir, '/', in_dirs(i).name, '.mat']);
gt_landmarks(:,:,int32(error)) = [];
preds(:,:,int32(error))=[];
end
vid_name = in_dirs(i).name;
save([output_dir, '/', vid_name], 'preds', 'gt_landmarks');
end
% [pocr_error, err_pp_pocr] = compute_error( gts_all, preds_all);

156
matlab_version/experiments_300VW/DCLM_300VW.m Normal file
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@@ -0,0 +1,156 @@
clear;
addpath('../PDM_helpers/');
addpath('../fitting/normxcorr2_mex_ALL');
addpath('../fitting/');
addpath('../CCNF/');
addpath('../models/');
output_dir = './DCLM_res/';
%% select database and load bb initializations
db_root = 'D:\Datasets\300VW_Dataset_2015_12_14\300VW_Dataset_2015_12_14/';
bb_root = '../..//matlab_runners/Feature Point Experiments/300VW_face_dets/';
extra_dir = 'D:\Datasets\300VW_Dataset_2015_12_14\extra';
in_dirs = dir(db_root);
in_dirs = in_dirs(3:end);
in_dirs = in_dirs(1:end-2);
preds_all = [];
gts_all = [];
%% loading the patch experts and the PDM
clmParams = struct;
clmParams.window_size = [25,25; 23,23; 21,21; 21,21];
clmParams.numPatchIters = size(clmParams.window_size,1);
[patches] = Load_DCLM_Patch_Experts( '../models/general/', 'dccnf_patches_*_general.mat', [], [], clmParams);
% 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(pdm.M) / 3;
clmParams.regFactor = [35, 27, 20, 20];
clmParams.sigmaMeanShift = [1.25, 1.375, 1.5, 1.5];
clmParams.tikhonov_factor = [0,0,0,0];
clmParams.startScale = 1;
clmParams.num_RLMS_iter = 10;
clmParams.fTol = 0.01;
clmParams.useMultiScale = true;
clmParams.use_multi_modal = 1;
clmParams.multi_modal_types = patches(1).multi_modal_types;
clmParams.numPatchIters = 4;
multi_view = true;
verbose = true;
%% Select video
for i=1:numel(in_dirs)
in_file_name = [db_root '/', in_dirs(i).name, '/vid.avi'];
vid = VideoReader(in_file_name);
bounding_boxes = dlmread([bb_root, in_dirs(i).name, '_dets.txt'], ',');
preds = [];
n_frames = size(bounding_boxes,1);
for f=1:n_frames
input_image = readFrame(vid);
reset = true;
%% Initialize from detected bounding boxes every 30 frames
if (mod(f-1, 30) == 0)
ind = min(f, size(bounding_boxes,1));
bb = bounding_boxes(ind, :);
% If no face detected use the closest detected BB
if(bb(3) == 0)
ind_next = ind + find(bounding_boxes(ind+1:end,3), 1);
if(isempty(bounding_boxes(ind_next)) || bounding_boxes(ind_next,3)==0)
ind_next = find(bounding_boxes(1:ind,3), 1, 'last');
end
bb = bounding_boxes(ind_next, :);
end
bb(3) = bb(1) + bb(3);
bb(4) = bb(2) + bb(4);
reset = true;
else
reset = false;
end
% have a multi-view version for initialization, otherwise use
% previous shape
if(reset && multi_view)
clmParams.window_size = [25,25; 23,23; 21,21; 21,21];
clmParams.numPatchIters = 4;
clmParams.startScale = 1;
views = [0,0,0; 0,-45,0; -30,0,0; 0,45,0; 30,0,0];
views = views * pi/180;
shapes = zeros(num_points, 2, size(views,1));
ls = zeros(size(views,1),1);
lmark_lhoods = zeros(num_points,size(views,1));
views_used = zeros(num_points,size(views,1));
% Find the best orientation
for v = 1:size(views,1)
[shapes(:,:,v),g_param,l_param,ls(v),lmark_lhoods(:,v),views_used(v)] = Fitting_from_bb(input_image, [], bb, pdm, patches, clmParams, 'orientation', views(v,:));
end
[lhood, v_ind] = max(ls);
lmark_lhood = lmark_lhoods(:,v_ind);
shape = shapes(:,:,v_ind);
view_used = views_used(v);
else
clmParams.window_size = [23,23; 21,21; 19,19; 17,17];
clmParams.numPatchIters = 3;
clmParams.startScale = 2;
[shape,g_param,l_param,lhood,lmark_lhood,view_used] = Fitting_from_bb(input_image, [], bb, pdm, patches, clmParams, 'gparam', g_param, 'lparam', l_param);
end
preds = cat(3, preds, shape);
%% plot the result
imshow(input_image);
hold on;
plot(shape(:,1), shape(:,2), '.r');
% rectangle('Position', [bb(2), bb(1), bb(4), bb(3)]);
hold off;
drawnow expose
end
%% Grab the ground truth
fps_all = dir([db_root, '/', in_dirs(i).name, '/annot/*.pts']);
gt_landmarks = zeros([68, 2, size(fps_all)]);
for k = 1:size(fps_all)
gt_landmarks_frame = dlmread([db_root, '/', in_dirs(i).name, '/annot/', fps_all(k).name], ' ', 'A4..B71');
gt_landmarks(:,:,k) = gt_landmarks_frame;
end
if(size(gt_landmarks,3) ~= size(preds,3))
fprintf('something went wrong with vid %d\n', i);
end
% Remove unreliable frames
if(exist([extra_dir, '/', in_dirs(i).name, '.mat'], 'file'))
load([extra_dir, '/', in_dirs(i).name, '.mat']);
gt_landmarks(:,:,int32(error)) = [];
preds(:,:,int32(error))=[];
end
vid_name = in_dirs(i).name;
save([output_dir, '/', vid_name], 'preds', 'gt_landmarks');
end
% [pocr_error, err_pp_pocr] = compute_error( gts_all, preds_all);

109
matlab_version/experiments_300VW/Display_300VW_results.m Normal file
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@@ -0,0 +1,109 @@
clear
%% Gather predictions and ground truth
% d_loc = '300VW_features/';
% extra_dir = 'D:\Datasets\300VW_Dataset_2015_12_14\extra';
%
%
% files_yt = dir([d_loc, '/*.txt']);
% preds_all = [];
% gts_all = [];
% confs = [];
% for i = 1:numel(files_yt)
% [~, name, ~] = fileparts(files_yt(i).name);
% pred_landmarks = dlmread([d_loc, files_yt(i).name], ',', 1, 0);
% conf = pred_landmarks(:,3);
% pred_landmarks = pred_landmarks(:,5:end);
%
% xs = pred_landmarks(:, 1:end/2);
% ys = pred_landmarks(:, end/2+1:end);
% pred_landmarks = zeros([size(xs,2), 2, size(xs,1)]);
% pred_landmarks(:,1,:) = xs';
% pred_landmarks(:,2,:) = ys';
%
% name = name(1:end-3);
% fps_all = dir([database_root, '/', name, '/annot/*.pts']);
% gt_landmarks = zeros(size(pred_landmarks));
% for k = 1:size(fps_all)
%
% gt_landmarks_frame = dlmread([database_root, '/', name, '/annot/', fps_all(k).name], ' ', 'A4..B71');
% gt_landmarks(:,:,k) = gt_landmarks_frame;
% end
%
% if(size(pred_landmarks,3) ~= size(fps_all))
% fprintf('something wrong at vid %s, fps - %d, preds - %d\n', name, gt_landmarks);
% end
%
% % Remove unreliable frames
% if(exist([extra_dir, '/', name, '.mat'], 'file'))
% load([extra_dir, '/', name, '.mat']);
% gt_landmarks(:,:,int32(error)) = [];
% pred_landmarks(:,:,int32(error))=[];
% conf(int32(error)) = [];
% end
%
% preds_all = cat(3, preds_all, pred_landmarks);
% gts_all = cat(3, gts_all, gt_landmarks);
% confs = cat(1, confs, conf);
% end
%%
[clnf_error, err_pp_clnf] = compute_error( gts_all - 0.5, preds_all);
preds_all_49 = preds_all;
preds_all_49(65,:,:) = [];
preds_all_49(61,:,:) = [];
preds_all_49 = preds_all_49(18:end,:,:);
gts_all_49 = gts_all;
gts_all_49(65,:,:) = [];
gts_all_49(61,:,:) = [];
gts_all_49 = gts_all_49(18:end,:,:);
[clnf_error49, err_pp_clnf49] = compute_error( gts_all_49 - 0.5, preds_all_49);
filename = sprintf('results/300VW_CLNF');
save(filename, 'clnf_error', 'clnf_error49');
%%
line_width = 2;
% [error_x, error_y] = cummErrorCurve(clnf_error49);
% plot(error_x, error_y, 'DisplayName', 'OpenFace-49', 'LineWidth',line_width);
% hold on;
load('results/300VW_SDM.mat');
[error_x, error_y] = cummErrorCurve(sdm_error);
plot(error_x, error_y, 'DisplayName', 'SDM-49', 'LineWidth',line_width);
hold on;
load('results/300VW_chehra.mat');
[error_x, error_y] = cummErrorCurve(chehra_error);
plot(error_x, error_y, 'DisplayName', 'Chehra-49', 'LineWidth',line_width);
load('results/300VW_pocr.mat');
[error_x, error_y] = cummErrorCurve(pocr_error);
plot(error_x, error_y, 'DisplayName', 'PO-CR-49', 'LineWidth',line_width);
% Make it look nice and print to a pdf
set(gca,'xtick',[0:0.01:0.08])
xlim([0,0.08]);
xlabel('Size normalised shape RMS error of 49 landmarks','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
legend('show', 'Location', 'SouthEast');
print -dpdf results/300VWres_49_114vids.pdf
%%
line_width = 2;
[error_x, error_y] = cummErrorCurve(clnf_error);
plot(error_x, error_y, 'DisplayName', 'OpenFace-68', 'LineWidth',line_width);
hold on;
% Make it look nice and print to a pdf
set(gca,'xtick',[0:0.01:0.08])
xlim([0,0.08]);
xlabel('Size normalised shape RMS error of 68 landmarks','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
legend('show', 'Location', 'SouthEast');
print -dpdf results/300VWres_68_114vids.pdf

320
matlab_version/experiments_300VW/Display_300VW_results_cats.m Normal file
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clear
scrsz = get(0,'ScreenSize');
figure1 = figure('Position',[20 50 3*scrsz(3)/4 0.9*scrsz(4)]);
set(figure1,'Units','Inches');
pos = get(figure1,'Position');
set(figure1,'PaperPositionMode','Auto','PaperUnits','Inches','PaperSize',[pos(3), pos(4)])
% Create axes
axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 6;
hold on;
database_root = 'D:\Datasets\300VW_Dataset_2015_12_14\300VW_Dataset_2015_12_14/';
pocr_dir = 'D:\Dropbox\Dropbox\AAM\3rd party models\PO-CR\300VW/';
cfss_dir = 'C:\Users\tbaltrus\Documents\300W-CFSS/';
%% Gather predictions and ground truth
d_loc = '300VW_features/';
extra_dir = 'D:\Datasets\300VW_Dataset_2015_12_14\extra';
cat_1 = [ 114, 124, 125, 126, 150, 158, 401, 402, 505, 506, 507, 508, 509, 510, 511, 514, 515, 518, 519, 520, 521, 522, 524, 525, 537, 538, 540, 541, 546, 547, 548];
% cat_1 = [ 114, 124, 125, 126, 150, 158, 401, 402, 505, 506, 507, 508, 509, 510, 511, 514, 515, 518, 519, 520, 521, 522, 524, 525, 537];
cat_2 = [203, 208, 211, 212, 213, 214, 218, 224, 403, 404, 405, 406, 407, 408, 409, 412, 550, 551, 553];
cat_3 = [410, 411, 516, 517, 526, 528, 529, 530, 531, 533, 557, 558, 559, 562];
%%
load('results/cat_ids.mat');
%%
dclm_preds = zeros(68,2,0);
clnf_preds = zeros(68,2,0);
pocr_preds = zeros(49,2,0);
cfss_preds = zeros(68,2,0);
clm_preds = zeros(68,2,0);
labels = zeros(68,2,0);
% Load DCLM and CLNF results
for i=cat_1
load(['DCLM_res/', num2str(i)]);
dclm_preds = cat(3, dclm_preds, preds);
load(['CLNF_res/', num2str(i)]);
clnf_preds = cat(3, clnf_preds, preds);
load(['CLM_res/', num2str(i)]);
clm_preds = cat(3, clm_preds, preds);
labels = cat(3, labels, gt_landmarks);
load([pocr_dir, num2str(i)]);
pocr_preds = cat(3, pocr_preds, preds);
load([cfss_dir, num2str(i)]);
cfss_preds = cat(3, cfss_preds, preds);
end
dclm_preds = dclm_preds([1:60,62:64,66:end],:,:);
labels = labels([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:);
dclm_preds = dclm_preds(18:end,:,:);
dclm_error = compute_error(labels, dclm_preds);
[error_x, error_y] = cummErrorCurve(dclm_error);
plot(error_x, error_y, 'r', 'DisplayName', 'DCLM', 'LineWidth',line_width);
hold on;
clnf_preds = clnf_preds([1:60,62:64,66:end],:,:);
clnf_preds = clnf_preds(18:end,:,:);
clnf_error = compute_error(labels, clnf_preds);
[error_x, error_y] = cummErrorCurve(clnf_error);
plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
hold on;
load('results/300VW_SDM.mat');
[error_x, error_y] = cummErrorCurve(sdm_error(inds_cat1));
plot(error_x, error_y, 'DisplayName', 'SDM', 'LineWidth',line_width);
cfss_preds = cfss_preds([1:60,62:64,66:end],:,:);
cfss_preds = cfss_preds(18:end,:,:);
cfss_error = compute_error(labels, cfss_preds - 1);
[error_x, error_y] = cummErrorCurve(cfss_error);
plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
hold on;
load('results/300VW_chehra.mat');
[error_x, error_y] = cummErrorCurve(chehra_error(inds_cat1));
plot(error_x, error_y, 'DisplayName', 'Chehra', 'LineWidth',line_width);
pocr_error = compute_error(labels, pocr_preds);
[error_x, error_y] = cummErrorCurve(pocr_error);
plot(error_x, error_y, 'DisplayName', 'PO-CR', 'LineWidth',line_width);
clm_preds = clm_preds([1:60,62:64,66:end],:,:);
clm_preds = clm_preds(18:end,:,:);
clm_error = compute_error(labels, clm_preds);
[error_x, error_y] = cummErrorCurve(clm_error);
plot(error_x, error_y, 'DisplayName', 'CLM+', 'LineWidth',line_width);
hold on;
% Make it look nice and print to a pdf
set(gca,'xtick',[0.01:0.01:0.07])
xlim([0.01,0.07]);
xlabel('IOD normalized MSE','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
ax=legend('show', 'Location', 'SouthEast');
ax.FontSize = 50;
print -dpdf results/300VWres_49_cat1.pdf
%%
dclm_preds = zeros(68,2,0);
clnf_preds = zeros(68,2,0);
pocr_preds = zeros(49,2,0);
cfss_preds = zeros(68,2,0);
clm_preds = zeros(68,2,0);
labels = zeros(68,2,0);
% Load DCLM and CLNF results
for i=cat_2
load(['DCLM_res/', num2str(i)]);
dclm_preds = cat(3, dclm_preds, preds);
load(['CLNF_res/', num2str(i)]);
clnf_preds = cat(3, clnf_preds, preds);
load(['CLM_res/', num2str(i)]);
clm_preds = cat(3, clm_preds, preds);
labels = cat(3, labels, gt_landmarks);
load([pocr_dir, num2str(i)]);
pocr_preds = cat(3, pocr_preds, preds);
load([cfss_dir, num2str(i)]);
cfss_preds = cat(3, cfss_preds, preds);
end
scrsz = get(0,'ScreenSize');
figure1 = figure('Position',[20 50 3*scrsz(3)/4 0.9*scrsz(4)]);
set(figure1,'Units','Inches');
pos = get(figure1,'Position');
set(figure1,'PaperPositionMode','Auto','PaperUnits','Inches','PaperSize',[pos(3), pos(4)])
% Create axes
axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 6;
hold on;
dclm_preds = dclm_preds([1:60,62:64,66:end],:,:);
labels = labels([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:);
dclm_preds = dclm_preds(18:end,:,:);
dclm_error = compute_error(labels, dclm_preds);
[error_x, error_y] = cummErrorCurve(dclm_error);
plot(error_x, error_y, 'r', 'DisplayName', 'DCLM', 'LineWidth',line_width);
hold on;
clnf_preds = clnf_preds([1:60,62:64,66:end],:,:);
clnf_preds = clnf_preds(18:end,:,:);
clnf_error = compute_error(labels, clnf_preds);
[error_x, error_y] = cummErrorCurve(clnf_error);
plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
hold on;
load('results/300VW_SDM.mat');
[error_x, error_y] = cummErrorCurve(sdm_error(inds_cat2));
plot(error_x, error_y, 'DisplayName', 'SDM', 'LineWidth',line_width);
cfss_preds = cfss_preds([1:60,62:64,66:end],:,:);
cfss_preds = cfss_preds(18:end,:,:);
cfss_error = compute_error(labels, cfss_preds - 1);
[error_x, error_y] = cummErrorCurve(cfss_error);
plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
load('results/300VW_chehra.mat');
[error_x, error_y] = cummErrorCurve(chehra_error(inds_cat2));
plot(error_x, error_y, 'DisplayName', 'Chehra', 'LineWidth',line_width);
pocr_error = compute_error(labels, pocr_preds);
[error_x, error_y] = cummErrorCurve(pocr_error);
plot(error_x, error_y, 'DisplayName', 'PO-CR', 'LineWidth',line_width);
clm_preds = clm_preds([1:60,62:64,66:end],:,:);
clm_preds = clm_preds(18:end,:,:);
clm_error = compute_error(labels, clm_preds);
[error_x, error_y] = cummErrorCurve(clm_error);
plot(error_x, error_y, 'DisplayName', 'CLM+', 'LineWidth',line_width);
hold on;
% Make it look nice and print to a pdf
set(gca,'xtick',[0.01:0.01:0.07])
xlim([0.01,0.07]);
xlabel('IOD normalized MSE','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
ax=legend('show', 'Location', 'SouthEast');
ax.FontSize = 50;
print -dpdf results/300VWres_49_cat2.pdf
%%
dclm_preds = zeros(68,2,0);
clnf_preds = zeros(68,2,0);
pocr_preds = zeros(49,2,0);
cfss_preds = zeros(68,2,0);
clm_preds = zeros(68,2,0);
labels = zeros(68,2,0);
% Load DCLM and CLNF results
for i=cat_3
load(['DCLM_res/', num2str(i)]);
dclm_preds = cat(3, dclm_preds, preds);
load(['CLNF_res/', num2str(i)]);
clnf_preds = cat(3, clnf_preds, preds);
load(['CLM_res/', num2str(i)]);
clm_preds = cat(3, clm_preds, preds);
labels = cat(3, labels, gt_landmarks);
load([pocr_dir, num2str(i)]);
pocr_preds = cat(3, pocr_preds, preds);
load([cfss_dir, num2str(i)]);
cfss_preds = cat(3, cfss_preds, preds);
end
scrsz = get(0,'ScreenSize');
figure1 = figure('Position',[20 50 3*scrsz(3)/4 0.9*scrsz(4)]);
set(figure1,'Units','Inches');
pos = get(figure1,'Position');
set(figure1,'PaperPositionMode','Auto','PaperUnits','Inches','PaperSize',[pos(3), pos(4)])
% Create axes
axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 6;
hold on;
dclm_preds = dclm_preds([1:60,62:64,66:end],:,:);
labels = labels([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:);
dclm_preds = dclm_preds(18:end,:,:);
dclm_error = compute_error(labels, dclm_preds );
[error_x, error_y] = cummErrorCurve(dclm_error);
plot(error_x, error_y, 'r', 'DisplayName', 'DCLM', 'LineWidth',line_width);
hold on;
clnf_preds = clnf_preds([1:60,62:64,66:end],:,:);
clnf_preds = clnf_preds(18:end,:,:);
clnf_error = compute_error(labels, clnf_preds );
[error_x, error_y] = cummErrorCurve(clnf_error);
plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
hold on;
load('results/300VW_SDM.mat');
[error_x, error_y] = cummErrorCurve(sdm_error(inds_cat3));
plot(error_x, error_y, 'DisplayName', 'SDM', 'LineWidth',line_width);
cfss_preds = cfss_preds([1:60,62:64,66:end],:,:);
cfss_preds = cfss_preds(18:end,:,:);
cfss_error = compute_error(labels, cfss_preds - 1);
[error_x, error_y] = cummErrorCurve(cfss_error);
plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
load('results/300VW_chehra.mat');
[error_x, error_y] = cummErrorCurve(chehra_error(inds_cat3));
plot(error_x, error_y, 'DisplayName', 'Chehra', 'LineWidth',line_width);
pocr_error = compute_error(labels, pocr_preds);
[error_x, error_y] = cummErrorCurve(pocr_error);
plot(error_x, error_y, 'DisplayName', 'PO-CR', 'LineWidth',line_width);
clm_preds = clm_preds([1:60,62:64,66:end],:,:);
clm_preds = clm_preds(18:end,:,:);
clm_error = compute_error(labels, clm_preds);
[error_x, error_y] = cummErrorCurve(clm_error);
plot(error_x, error_y, 'DisplayName', 'CLM+', 'LineWidth',line_width);
hold on;
% Make it look nice and print to a pdf
set(gca,'xtick',[0.01:0.01:0.07])
xlim([0.01,0.07]);
xlabel('IOD normalized MSE','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
ax=legend('show', 'Location', 'SouthEast');
ax.FontSize = 50;
print -dpdf results/300VWres_49_cat3.pdf
%%

235
matlab_version/experiments_300VW/Display_300VW_results_cats_68.m Normal file
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clear
scrsz = get(0,'ScreenSize');
figure1 = figure('Position',[20 50 3*scrsz(3)/4 0.9*scrsz(4)]);
set(figure1,'Units','Inches');
pos = get(figure1,'Position');
set(figure1,'PaperPositionMode','Auto','PaperUnits','Inches','PaperSize',[pos(3), pos(4)])
% Create axes
axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 6;
hold on;
database_root = 'D:\Datasets\300VW_Dataset_2015_12_14\300VW_Dataset_2015_12_14/';
pocr_dir = 'D:\Dropbox\Dropbox\AAM\3rd party models\PO-CR\300VW/';
cfss_dir = 'C:\Users\tbaltrus\Documents\300W-CFSS/';
%% Gather predictions and ground truth
d_loc = '300VW_features/';
extra_dir = 'D:\Datasets\300VW_Dataset_2015_12_14\extra';
cat_1 = [ 114, 124, 125, 126, 150, 158, 401, 402, 505, 506, 507, 508, 509, 510, 511, 514, 515, 518, 519, 520, 521, 522, 524, 525, 537, 538, 540, 541, 546, 547, 548];
% cat_1 = [ 114, 124, 125, 126, 150, 158, 401, 402, 505, 506, 507, 508, 509, 510, 511, 514, 515, 518, 519, 520, 521, 522, 524, 525, 537];
cat_2 = [203, 208, 211, 212, 213, 214, 218, 224, 403, 404, 405, 406, 407, 408, 409, 412, 550, 551, 553];
%cat_3 = [410, 411, 516, 517, 526, 528, 529, 530, 531, 533, 557, 558, 559, 562];
cat_3 = [410, 411, 516, 517, 526, 528];
%%
load('results/cat_ids.mat');
cfss_dir = 'C:\Users\tbaltrus\Documents\300W-CFSS/';
%%
dclm_preds = zeros(68,2,0);
clnf_preds = zeros(68,2,0);
cfss_preds = zeros(68,2,0);
clm_preds = zeros(68,2,0);
labels = zeros(68,2,0);
% Load DCLM and CLNF results
for i=cat_1
load(['DCLM_res/', num2str(i)]);
dclm_preds = cat(3, dclm_preds, preds);
load(['CLNF_res/', num2str(i)]);
clnf_preds = cat(3, clnf_preds, preds);
load(['CLM_res/', num2str(i)]);
clm_preds = cat(3, clm_preds, preds);
labels = cat(3, labels, gt_landmarks);
load([cfss_dir, num2str(i)]);
cfss_preds = cat(3, cfss_preds, preds);
end
dclm_error = compute_error(labels, dclm_preds);
[error_x, error_y] = cummErrorCurve(dclm_error);
plot(error_x, error_y, 'r', 'DisplayName', 'DCLM', 'LineWidth',line_width);
hold on;
clnf_error = compute_error(labels, clnf_preds);
[error_x, error_y] = cummErrorCurve(clnf_error);
plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
hold on;
clm_error = compute_error(labels, clm_preds);
[error_x, error_y] = cummErrorCurve(clm_error);
plot(error_x, error_y, 'DisplayName', 'CLM+', 'LineWidth',line_width);
hold on;
cfss_error = compute_error(labels, cfss_preds - 1);
[error_x, error_y] = cummErrorCurve(cfss_error);
plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
% Make it look nice and print to a pdf
set(gca,'xtick',[0.01:0.02:0.09])
xlim([0.01,0.09]);
xlabel('IOD normalized MSE','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
ax=legend('show', 'Location', 'SouthEast');
ax.FontSize = 50;
print -dpdf results/300VWres_68_cat1.pdf
%%
dclm_preds = zeros(68,2,0);
clnf_preds = zeros(68,2,0);
cfss_preds = zeros(68,2,0);
clm_preds = zeros(68,2,0);
labels = zeros(68,2,0);
% Load DCLM and CLNF results
for i=cat_2
load(['DCLM_res/', num2str(i)]);
dclm_preds = cat(3, dclm_preds, preds);
load(['CLNF_res/', num2str(i)]);
clnf_preds = cat(3, clnf_preds, preds);
load(['CLM_res/', num2str(i)]);
clm_preds = cat(3, clm_preds, preds);
labels = cat(3, labels, gt_landmarks);
load([cfss_dir, num2str(i)]);
cfss_preds = cat(3, cfss_preds, preds);
end
scrsz = get(0,'ScreenSize');
figure1 = figure('Position',[20 50 3*scrsz(3)/4 0.9*scrsz(4)]);
set(figure1,'Units','Inches');
pos = get(figure1,'Position');
set(figure1,'PaperPositionMode','Auto','PaperUnits','Inches','PaperSize',[pos(3), pos(4)])
% Create axes
axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 6;
hold on;
dclm_error = compute_error(labels, dclm_preds);
[error_x, error_y] = cummErrorCurve(dclm_error);
plot(error_x, error_y, 'r', 'DisplayName', 'DCLM', 'LineWidth',line_width);
hold on;
clnf_error = compute_error(labels, clnf_preds);
[error_x, error_y] = cummErrorCurve(clnf_error);
plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
hold on;
clm_error = compute_error(labels, clm_preds);
[error_x, error_y] = cummErrorCurve(clm_error);
plot(error_x, error_y, 'DisplayName', 'CLM+', 'LineWidth',line_width);
hold on;
cfss_error = compute_error(labels, cfss_preds - 1);
[error_x, error_y] = cummErrorCurve(cfss_error);
plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
% Make it look nice and print to a pdf
set(gca,'xtick',[0.01:0.02:0.09])
xlim([0.01,0.09]);
xlabel('IOD normalized MSE','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
ax=legend('show', 'Location', 'SouthEast');
ax.FontSize = 50;
print -dpdf results/300VWres_68_cat2.pdf
%%
dclm_preds = zeros(68,2,0);
clnf_preds = zeros(68,2,0);
cfss_preds = zeros(68,2,0);
clm_preds = zeros(68,2,0);
labels = zeros(68,2,0);
% Load DCLM and CLNF results
for i=cat_3
load(['DCLM_res/', num2str(i)]);
dclm_preds = cat(3, dclm_preds, preds);
load(['CLNF_res/', num2str(i)]);
clnf_preds = cat(3, clnf_preds, preds);
load(['CLM_res/', num2str(i)]);
clm_preds = cat(3, clm_preds, preds);
labels = cat(3, labels, gt_landmarks);
load([cfss_dir, num2str(i)]);
cfss_preds = cat(3, cfss_preds, preds);
end
scrsz = get(0,'ScreenSize');
figure1 = figure('Position',[20 50 3*scrsz(3)/4 0.9*scrsz(4)]);
set(figure1,'Units','Inches');
pos = get(figure1,'Position');
set(figure1,'PaperPositionMode','Auto','PaperUnits','Inches','PaperSize',[pos(3), pos(4)])
% Create axes
axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 6;
hold on;
dclm_error = compute_error(labels, dclm_preds );
[error_x, error_y] = cummErrorCurve(dclm_error);
plot(error_x, error_y, 'r', 'DisplayName', 'DCLM', 'LineWidth',line_width);
hold on;
clnf_error = compute_error(labels, clnf_preds );
[error_x, error_y] = cummErrorCurve(clnf_error);
plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
hold on;
clm_error = compute_error(labels, clm_preds);
[error_x, error_y] = cummErrorCurve(clm_error);
plot(error_x, error_y, 'DisplayName', 'CLM+', 'LineWidth',line_width);
hold on;
cfss_error = compute_error(labels, cfss_preds - 1);
[error_x, error_y] = cummErrorCurve(cfss_error);
plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
% Make it look nice and print to a pdf
set(gca,'xtick',[0.01:0.02:0.09])
xlim([0.01,0.09]);
xlabel('IOD normalized MSE','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
ax=legend('show', 'Location', 'SouthEast');
ax.FontSize = 50;
print -dpdf results/300VWres_68_cat3.pdf
%%

41
matlab_version/experiments_300VW/compute_error.m Normal file
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function [ error_per_image, err_pp, err_pp_dim ] = compute_error( ground_truth_all, detected_points_all )
%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
num_of_images = size(ground_truth_all,3);
num_of_points = size(ground_truth_all,1);
error_per_image = zeros(num_of_images,1);
err_pp = zeros(num_of_images, num_of_points);
err_pp_dim = zeros(num_of_images, num_of_points, 2);
for i =1:num_of_images
detected_points = detected_points_all(:,:,i);
ground_truth_points = ground_truth_all(:,:,i);
if(num_of_points == 66 || num_of_points == 68)
interocular_distance = norm(ground_truth_points(37,:)-ground_truth_points(46,:));
else
interocular_distance = norm(ground_truth_points(37-17,:)-ground_truth_points(46-17,:));
end
sum=0;
for j=1:num_of_points
sum = sum+norm(detected_points(j,:)-ground_truth_points(j,:));
err_pp(i,j) = norm(detected_points(j,:)-ground_truth_points(j,:));
err_pp_dim(i,j,1) = detected_points(j,1)-ground_truth_points(j,1);
err_pp_dim(i,j,2) = detected_points(j,2)-ground_truth_points(j,2);
end
error_per_image(i) = sum/(num_of_points*interocular_distance);
err_pp(i,:) = err_pp(i,:) ./ interocular_distance;
err_pp_dim(i,:) = err_pp_dim(i,:) ./ interocular_distance;
end
end

24
matlab_version/experiments_300VW/cummErrorCurve.m Normal file
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function [x, y] = cummErrorCurve( errorVec )
%CUMMERRORCURVE Summary of this function goes here
% Detailed explanation goes here
spacing = 0.0005;
sampling = [0:spacing:max(errorVec)];
x = sampling;
y = zeros(numel(sampling,1));
for i=1:numel(sampling)
y(i) = sum(errorVec < sampling(i)) / numel(errorVec);
end
for i=1:200
x = cat(2, x, x(end)+spacing);
y = cat(2, y, 1);
end
end

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matlab_version/experiments_JANUS/Display_dclm_results.m
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@@ -14,20 +14,6 @@ axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 6;
hold on;
load('results/results_wild_clnf_general_final_inner.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:);
% center the pixel
shapes = shapes(18:end,:,:);
clnf_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(clnf_error);
plot(error_x, error_y, 'b','DisplayName', 'CLNF (ECCV 14)', 'LineWidth',line_width);
hold on;
load('results/results_wild_dclm_general.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
@@ -42,16 +28,59 @@ clnf_error = compute_error( labels, shapes);
plot(error_x, error_y, 'r','DisplayName', 'DCLM', 'LineWidth',line_width);
hold on;
load('results/JANUS_pocr.mat');
load('results/results_wild_clnf_general_final_inner.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:);
% center the pixel
shapes = shapes(18:end,:,:);
clnf_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(clnf_error);
plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
hold on;
load('results/JANUS-CFSS.mat');
% center the pixel
shapes_all = zeros(68, 2, size(estimatedPose,1));
for i = 1:size(estimatedPose,1)
shapes_all(:,1,i) = estimatedPose(i,1:68)';
shapes_all(:,2,i) = estimatedPose(i,69:end)';
end
shapes_all = shapes_all([1:60,62:64,66:end],:,:);
shapes_all = shapes_all(18:end,:,:);
cfss_error = compute_error(labels, shapes_all(:,:,:) - 0.5);
[error_x, error_y] = cummErrorCurve(cfss_error);
plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
hold on;
load('results/JANUS_sdm.mat');
% center the pixel
shapes_all = experiments.shapes;
sdm_error = compute_error(labels, shapes_all + 0.5);
[error_x, error_y] = cummErrorCurve(sdm_error);
plot(error_x, error_y, 'DisplayName', 'SDM', 'LineWidth',line_width);
hold on;
load('results/JANUS_pocr.mat');
shapes_all = experiments.shapes;
pocr_error = compute_error(labels, shapes_all);
[error_x, error_y] = cummErrorCurve(pocr_error);
plot(error_x, error_y, '.-g','DisplayName', 'PO-CR (CVPR 15)', 'LineWidth',line_width);
plot(error_x, error_y,'DisplayName', 'PO-CR', 'LineWidth',line_width);
hold on;
% load('results/intraface_wild_resize.mat');
@@ -112,9 +141,9 @@ hold on;
%
% plot(error_x, error_y, '-.k','DisplayName', 'DRMF (CVPR 13)', 'LineWidth',line_width);
set(gca,'xtick',[0:0.05:0.15])
xlim([0,0.15]);
xlabel('Size normalised shape RMS error','FontName','Helvetica');
set(gca,'xtick',[0:0.02:0.10])
xlim([0.02,0.10]);
xlabel('Size normalised MSE','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
% title('Fitting in the wild without outline','FontSize',60,'FontName','Helvetica');
@@ -139,18 +168,6 @@ axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 6;
hold on;
load('results/results_wild_clnf_general_final_inner.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
% center the pixel
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
clnf_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(clnf_error);
hold on;
plot(error_x, error_y, 'DisplayName', 'CLNF (ECCV 14)', 'LineWidth',line_width);
load('results/results_wild_dclm_general.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
% center the pixel
@@ -161,7 +178,21 @@ clnf_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(clnf_error);
hold on;
plot(error_x, error_y, 'DisplayName', 'DCLM', 'LineWidth',line_width);
plot(error_x, error_y, 'r', 'DisplayName', 'DCLM', 'LineWidth',line_width);
load('results/results_wild_clnf_general_final_inner.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
% center the pixel
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
clnf_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(clnf_error);
hold on;
plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
% load('results/zhu_wild.mat');
%
@@ -200,9 +231,25 @@ plot(error_x, error_y, 'DisplayName', 'DCLM', 'LineWidth',line_width);
%
% plot(error_x, error_y, '-.k','DisplayName', 'DRMF', 'LineWidth',line_width);
set(gca,'xtick',[0:0.05:0.15])
xlim([0,0.15]);
xlabel('Size normalised shape RMS error','FontName','Helvetica');
load('results/JANUS-CFSS.mat');
% center the pixel
shapes_all = zeros(68, 2, size(estimatedPose,1));
for i = 1:size(estimatedPose,1)
shapes_all(:,1,i) = estimatedPose(i,1:68)';
shapes_all(:,2,i) = estimatedPose(i,69:end)';
end
cfss_error = compute_error(labels, shapes_all(:,:,:) - 0.5);
[error_x, error_y] = cummErrorCurve(cfss_error);
plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
hold on;
set(gca,'xtick',[0:0.02:0.10])
xlim([0.02,0.10]);
xlabel('Size normalised MSE','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
%title('Fitting in the wild','FontSize',60,'FontName','Helvetica');

260
matlab_version/experiments_JANUS/Display_janus_results_cvpr2017.m Normal file
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@@ -0,0 +1,260 @@
clear
%%
scrsz = get(0,'ScreenSize');
figure1 = figure('Position',[20 50 3*scrsz(3)/4 0.9*scrsz(4)]);
set(figure1,'Units','Inches');
pos = get(figure1,'Position');
set(figure1,'PaperPositionMode','Auto','PaperUnits','Inches','PaperSize',[pos(3), pos(4)])
% Create axes
axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 6;
hold on;
load('results/results_wild_dclm_general.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:);
% center the pixel
shapes = shapes(18:end,:,:);
clnf_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(clnf_error);
plot(error_x, error_y, 'r','DisplayName', 'DCLM', 'LineWidth',line_width);
hold on;
load('results/results_wild_clnf_general_final_inner.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:);
% center the pixel
shapes = shapes(18:end,:,:);
clnf_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(clnf_error);
plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
hold on;
load('results/JANUS-CFSS.mat');
% center the pixel
shapes_all = zeros(68, 2, size(estimatedPose,1));
for i = 1:size(estimatedPose,1)
shapes_all(:,1,i) = estimatedPose(i,1:68)';
shapes_all(:,2,i) = estimatedPose(i,69:end)';
end
shapes_all = shapes_all([1:60,62:64,66:end],:,:);
shapes_all = shapes_all(18:end,:,:);
cfss_error = compute_error(labels, shapes_all(:,:,:) - 0.5);
[error_x, error_y] = cummErrorCurve(cfss_error);
plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
hold on;
load('results/JANUS_sdm.mat');
% center the pixel
shapes_all = experiments.shapes;
sdm_error = compute_error(labels, shapes_all + 0.5);
[error_x, error_y] = cummErrorCurve(sdm_error);
plot(error_x, error_y, 'DisplayName', 'SDM', 'LineWidth',line_width);
hold on;
load('results/JANUS_pocr.mat');
shapes_all = experiments.shapes;
pocr_error = compute_error(labels, shapes_all);
[error_x, error_y] = cummErrorCurve(pocr_error);
plot(error_x, error_y,'DisplayName', 'PO-CR', 'LineWidth',line_width);
hold on;
% load('results/intraface_wild_resize.mat');
% labels_all = labels_all(18:end,:,detected);
% % center the pixel
% shapes_all = shapes_all(18:end,:,detected) + 0.5;
%
% intraface_wild_error = compute_error(labels_all, shapes_all);
%
% [error_x, error_y] = cummErrorCurve(intraface_wild_error);
%
% plot(error_x, error_y, '.-g','DisplayName', 'SDM (CVPR 13)', 'LineWidth',line_width);
% hold on;
% load('results/GNDPM_300W.mat');
% % center the pixel
% shapes_all = shapes_all(:,:,detected) + 1;
% labels_all = labels_all(:,:,detected);
%
% gndpm_wild_error = compute_error(labels_all, shapes_all);
%
% [error_x, error_y] = cummErrorCurve(gndpm_wild_error);
%
% plot(error_x, error_y, '-.','DisplayName', 'GNDPM (CVPR 14)', 'LineWidth',line_width);
% hold on;
% load('results/zhu_wild.mat');
% labels_all = labels_all(18:end,:,detected);
% shapes_all = shapes_all(18:end,:,detected);
%
% zhu_wild_error = compute_error(labels_all, shapes_all);
%
% [error_x, error_y] = cummErrorCurve(zhu_wild_error);
%
% plot(error_x, error_y, '.-c','DisplayName', 'Tree based (CVPR 12)', 'LineWidth',line_width);
% load('results/results_wild_clm.mat');
% labels = experiments.labels([1:60,62:64,66:end],:,detected);
% shapes = experiments.shapes([1:60,62:64,66:end],:,detected);
% labels = labels(18:end,:,:);
% % center the pixel
% shapes = shapes(18:end,:,:) + 0.5;
%
% clm_error = compute_error( labels, shapes);
%
% [error_x, error_y] = cummErrorCurve(clm_error);
%
% plot(error_x, error_y, '--b','DisplayName', 'CLM+', 'LineWidth',line_width);
% load('results/drmf_wild.mat');
% labels_all = labels_all(18:end,:,detected);
% shapes_all = shapes_all(18:end,:,detected);
%
% drmf_error = compute_error(labels_all, shapes_all);
%
% [error_x, error_y] = cummErrorCurve(drmf_error);
%
% plot(error_x, error_y, '-.k','DisplayName', 'DRMF (CVPR 13)', 'LineWidth',line_width);
set(gca,'xtick',[0:0.02:0.10])
xlim([0.02,0.10]);
xlabel('Size normalised MSE','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
% title('Fitting in the wild without outline','FontSize',60,'FontName','Helvetica');
leg = legend('show', 'Location', 'SouthEast');
set(leg,'FontSize',30)
print -dpdf results/Janus-no-outline.pdf
print -dpng results/Janus-no-outline.png
%%
scrsz = get(0,'ScreenSize');
figure1 = figure('Position',[20 50 3*scrsz(3)/4 0.9*scrsz(4)]);
set(figure1,'Units','Inches');
pos = get(figure1,'Position');
set(figure1,'PaperPositionMode','Auto','PaperUnits','Inches','PaperSize',[pos(3), pos(4)])
% Create axes
axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 6;
hold on;
load('results/results_wild_dclm_general.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
% center the pixel
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
clnf_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(clnf_error);
hold on;
plot(error_x, error_y, 'r', 'DisplayName', 'DCLM', 'LineWidth',line_width);
load('results/results_wild_clnf_general_final_inner.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
% center the pixel
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
clnf_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(clnf_error);
hold on;
plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
% load('results/zhu_wild.mat');
%
% zhu_wild_error = compute_error(labels_all(:,:,:), shapes_all(:,:,:));
%
% [error_x, error_y] = cummErrorCurve(zhu_wild_error);
%
% plot(error_x, error_y, '.-c','DisplayName', 'Zhu et al.', 'LineWidth',line_width);
%
% load('results/yu_wild.mat');
%
% yu_wild_error = compute_error(lmark_dets_all(:,:,:)-1, shapes_all(:,:,:));
% yu_wild_error(isnan(yu_wild_error)) = 1;
% yu_wild_error(isinf(yu_wild_error)) = 1;
%
% [error_x, error_y] = cummErrorCurve(yu_wild_error);
%
% plot(error_x, error_y, 'xg','DisplayName', 'Yu et al.', 'LineWidth',line_width);
%
% load('results/results_wild_clm.mat');
% experiments(1).labels = experiments(1).labels([1:60,62:64,66:end],:,:);
% % center the pixel
% experiments(1).shapes = experiments(1).shapes([1:60,62:64,66:end],:,:) + 0.5;
%
% clm_error = compute_error( experiments(1).labels, experiments(1).shapes);
%
% [error_x, error_y] = cummErrorCurve(clm_error);
%
% plot(error_x, error_y, '--b','DisplayName', 'CLM+', 'LineWidth',line_width);
%
% load('results/drmf_wild.mat');
%
% drmf_error = compute_error(labels_all, shapes_all);
%
% [error_x, error_y] = cummErrorCurve(drmf_error);
%
% plot(error_x, error_y, '-.k','DisplayName', 'DRMF', 'LineWidth',line_width);
load('results/JANUS-CFSS.mat');
% center the pixel
shapes_all = zeros(68, 2, size(estimatedPose,1));
for i = 1:size(estimatedPose,1)
shapes_all(:,1,i) = estimatedPose(i,1:68)';
shapes_all(:,2,i) = estimatedPose(i,69:end)';
end
cfss_error = compute_error(labels, shapes_all(:,:,:) - 0.5);
[error_x, error_y] = cummErrorCurve(cfss_error);
plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
hold on;
set(gca,'xtick',[0:0.02:0.10])
xlim([0.02,0.10]);
xlabel('Size normalised MSE','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
%title('Fitting in the wild','FontSize',60,'FontName','Helvetica');
legend('show', 'Location', 'SouthEast');
print -dpdf results/Janus-full.pdf
print -dpng results/Janus-full.png

79
matlab_version/experiments_JANUS/Script_CFSS_vis.m Normal file
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@@ -0,0 +1,79 @@
function Script_CFSS_vis()
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
root_test_data = 'D:/Datasets/janus_labeled';
[images, detections, labels] = Collect_JANUS_imgs(root_test_data);
inds = [50,78,151,195,220,619,883,237,258,299,384,442,526];
images = images(inds);
labels = labels(inds,:,:);
detections = detections(inds,:);
load('results/JANUS-CFSS.mat');
output_root = './all_fit_cvpr/';
for i=1:numel(images)
image = imread(images(i).img);
image_orig = image;
pred = estimatedPose(inds(i),:);
preds = cat(2, pred(1:68)', pred(69:end)');
actualShape = squeeze(labels(i,:,:));
v_points = sum(squeeze(labels(i,:,:)),2) > 0;
[height_img, width_img,~] = size(image_orig);
width = max(actualShape(v_points,1)) - min(actualShape(v_points,1));
height = max(actualShape(v_points,2)) - min(actualShape(v_points,2));
sz = (width+height)/2.0;
img_min_x = max(int32(min(actualShape(v_points,1))) - width/3,1);
img_max_x = min(int32(max(actualShape(v_points,1))) + width/3,width_img);
img_min_y = max(int32(min(actualShape(v_points,2))) - height/3,1);
img_max_y = min(int32(max(actualShape(v_points,2))) + height/3,height_img);
preds(:,1) = preds(:,1) - double(img_min_x);
preds(:,2) = preds(:,2) - double(img_min_y);
image_orig = image_orig(img_min_y:img_max_y, img_min_x:img_max_x, :);
scale = 600/sz;
image_orig = imresize(image_orig, scale);
% valid points to draw (not to draw
% occluded ones)
% f = figure('visible','off');
f = figure;
try
if(max(image_orig(:)) > 1)
imshow(double(image_orig)/255, 'Border', 'tight');
else
imshow(double(image_orig), 'Border', 'tight');
end
axis equal;
hold on;
plot(preds(:,1) * scale, preds(:,2) * scale,'.r','MarkerSize', int32(40));
plot(preds(:,1) * scale, preds(:,2) * scale,'.g','MarkerSize',int32(20));
% plot(preds(:,1), preds(:,2),'.w','MarkerSize',40);
% plot(preds(:,1), preds(:,2),'.k','MarkerSize',30);
print(f, '-r80', '-dpng', sprintf('%s/%s%d_cfss.png', output_root, 'fit', i));
% print(f, '-dpng', sprintf('%s/%s%d.png', output_root, 'fit', i));
% close(f);
hold off;
% drawnow expose
close(f);
catch warn
end
end

78
matlab_version/experiments_JANUS/Script_CLNF_vis.m Normal file
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@@ -0,0 +1,78 @@
function Script_CLNF_vis()
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
root_test_data = 'D:/Datasets/janus_labeled';
[images, detections, labels] = Collect_JANUS_imgs(root_test_data);
inds = [48,78,154,194,220,618,883,237,258,299,385,444,529];
images = images(inds);
labels = labels(inds,:,:);
detections = detections(inds,:);
load('results/results_wild_clnf_general_final_inner.mat');
output_root = './all_fit_cvpr/';
for i=1:numel(images)
image = imread(images(i).img);
image_orig = image;
preds = experiments.shapes(:,:,inds(i));
actualShape = squeeze(labels(i,:,:));
v_points = sum(squeeze(labels(i,:,:)),2) > 0;
[height_img, width_img,~] = size(image_orig);
width = max(actualShape(v_points,1)) - min(actualShape(v_points,1));
height = max(actualShape(v_points,2)) - min(actualShape(v_points,2));
sz = (width+height)/2.0;
img_min_x = max(int32(min(actualShape(v_points,1))) - width/3,1);
img_max_x = min(int32(max(actualShape(v_points,1))) + width/3,width_img);
img_min_y = max(int32(min(actualShape(v_points,2))) - height/3,1);
img_max_y = min(int32(max(actualShape(v_points,2))) + height/3,height_img);
preds(:,1) = preds(:,1) - double(img_min_x);
preds(:,2) = preds(:,2) - double(img_min_y);
image_orig = image_orig(img_min_y:img_max_y, img_min_x:img_max_x, :);
scale = 600/sz;
image_orig = imresize(image_orig, scale);
% valid points to draw (not to draw
% occluded ones)
% f = figure('visible','off');
f = figure;
try
if(max(image_orig(:)) > 1)
imshow(double(image_orig)/255, 'Border', 'tight');
else
imshow(double(image_orig), 'Border', 'tight');
end
axis equal;
hold on;
plot(preds(:,1) * scale, preds(:,2) * scale,'.r','MarkerSize', int32(40));
plot(preds(:,1) * scale, preds(:,2) * scale,'.g','MarkerSize',int32(20));
% plot(preds(:,1), preds(:,2),'.w','MarkerSize',40);
% plot(preds(:,1), preds(:,2),'.k','MarkerSize',30);
print(f, '-r80', '-dpng', sprintf('%s/%s%d_clnf.png', output_root, 'fit', i));
% print(f, '-dpng', sprintf('%s/%s%d.png', output_root, 'fit', i));
% close(f);
hold off;
% drawnow expose
close(f);
catch warn
end
end

78
matlab_version/experiments_JANUS/Script_DCLM_vis.m Normal file
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function Script_DCLM_vis()
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
root_test_data = 'D:/Datasets/janus_labeled';
[images, detections, labels] = Collect_JANUS_imgs(root_test_data);
inds = [48,78,154,194,220,618,883,237,258,299,385,444,529];
images = images(inds);
labels = labels(inds,:,:);
detections = detections(inds,:);
load('results/results_wild_dclm_general.mat');
output_root = './all_fit_cvpr/';
for i=1:numel(images)
image = imread(images(i).img);
image_orig = image;
preds = experiments.shapes(:,:,inds(i));
actualShape = squeeze(labels(i,:,:));
v_points = sum(squeeze(labels(i,:,:)),2) > 0;
[height_img, width_img,~] = size(image_orig);
width = max(actualShape(v_points,1)) - min(actualShape(v_points,1));
height = max(actualShape(v_points,2)) - min(actualShape(v_points,2));
sz = (width+height)/2.0;
img_min_x = max(int32(min(actualShape(v_points,1))) - width/3,1);
img_max_x = min(int32(max(actualShape(v_points,1))) + width/3,width_img);
img_min_y = max(int32(min(actualShape(v_points,2))) - height/3,1);
img_max_y = min(int32(max(actualShape(v_points,2))) + height/3,height_img);
preds(:,1) = preds(:,1) - double(img_min_x);
preds(:,2) = preds(:,2) - double(img_min_y);
image_orig = image_orig(img_min_y:img_max_y, img_min_x:img_max_x, :);
scale = 600/sz;
image_orig = imresize(image_orig, scale);
% valid points to draw (not to draw
% occluded ones)
% f = figure('visible','off');
f = figure;
try
if(max(image_orig(:)) > 1)
imshow(double(image_orig)/255, 'Border', 'tight');
else
imshow(double(image_orig), 'Border', 'tight');
end
axis equal;
hold on;
plot(preds(:,1) * scale, preds(:,2) * scale,'.r','MarkerSize', int32(40));
plot(preds(:,1) * scale, preds(:,2) * scale,'.g','MarkerSize',int32(20));
% plot(preds(:,1), preds(:,2),'.w','MarkerSize',40);
% plot(preds(:,1), preds(:,2),'.k','MarkerSize',30);
print(f, '-r80', '-dpng', sprintf('%s/%s%d_dclm.png', output_root, 'fit', i));
% print(f, '-dpng', sprintf('%s/%s%d.png', output_root, 'fit', i));
% close(f);
hold off;
% drawnow expose
close(f);
catch warn
end
end

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73
matlab_version/experiments_iccv_300w/Display_clnf_results_small.m
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clear
scrsz = get(0,'ScreenSize');
figure1 = figure('Position',[20 50 3*scrsz(3)/4 0.9*scrsz(4)]);
set(figure1,'Units','Inches');
pos = get(figure1,'Position');
set(figure1,'PaperPositionMode','Auto','PaperUnits','Inches','PaperSize',[pos(3), pos(4)])
% Create axes
axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 4;
hold on;
load('results/results_wild_clnf.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
% center the pixel
shapes = experiments.shapes([1:60,62:64,66:end],:,:) + 0.5;
clnf_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(clnf_error);
hold on;
plot(error_x, error_y, 'r','DisplayName', 'OpenFace', 'LineWidth',line_width);
load('results/zhu_wild.mat');
zhu_wild_error = compute_error(labels_all(:,:,:), shapes_all(:,:,:));
[error_x, error_y] = cummErrorCurve(zhu_wild_error);
plot(error_x, error_y, '.-c','DisplayName', 'Zhu et al.', 'LineWidth',line_width);
load('results/yu_wild.mat');
yu_wild_error = compute_error(lmark_dets_all(:,:,:)-1, shapes_all(:,:,:));
yu_wild_error(isnan(yu_wild_error)) = 1;
yu_wild_error(isinf(yu_wild_error)) = 1;
[error_x, error_y] = cummErrorCurve(yu_wild_error);
plot(error_x, error_y, 'xg','DisplayName', 'Yu et al.', 'LineWidth',line_width);
load('results/results_wild_clm.mat');
experiments(1).labels = experiments(1).labels([1:60,62:64,66:end],:,:);
% center the pixel
experiments(1).shapes = experiments(1).shapes([1:60,62:64,66:end],:,:) + 0.5;
clm_error = compute_error( experiments(1).labels, experiments(1).shapes);
[error_x, error_y] = cummErrorCurve(clm_error);
plot(error_x, error_y, '--b','DisplayName', 'CLM+', 'LineWidth',line_width);
load('results/drmf_wild.mat');
drmf_error = compute_error(labels_all, shapes_all);
[error_x, error_y] = cummErrorCurve(drmf_error);
plot(error_x, error_y, '-.k','DisplayName', 'DRMF', 'LineWidth',line_width);
set(gca,'xtick',[0:0.05:0.15])
xlim([0,0.15]);
xlabel('Size normalised shape RMS error','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
%title('Fitting in the wild','FontSize',60,'FontName','Helvetica');
legend('show', 'Location', 'SouthEast');
print -dpdf results/in-the-wild-comparison.pdf

233
matlab_version/experiments_iccv_300w/Display_dclm_results.m
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@@ -1,5 +1,6 @@
clear
inds = 338:1026;
%%
scrsz = get(0,'ScreenSize');
figure1 = figure('Position',[20 50 3*scrsz(3)/4 0.9*scrsz(4)]);
@@ -14,136 +15,130 @@ axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 6;
hold on;
load('results/intraface_wild_resize.mat');
labels_all = labels_all(18:end,:,:);
shapes_all = shapes_all(18:end,:,:);
load('../experiments_in_the_wild/results/results_wild_dclm_general.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:);
shapes = shapes(18:end,:,:) + 1.0;
intraface_wild_error = compute_error(labels_all, shapes_all);
dclm_error = compute_error( labels, shapes);
% removing faces that were not detected by intraface for fairness
detected = intraface_wild_error < 1;
[error_x, error_y] = cummErrorCurve(dclm_error(inds));
% load('results/results_wild_clnf.mat');
% labels = experiments.labels([1:60,62:64,66:end],:,detected);
% shapes = experiments.shapes([1:60,62:64,66:end],:,detected);
% labels = labels(18:end,:,:);
% % center the pixel
% shapes = shapes(18:end,:,:) + 0.5;
%
% clnf_error = compute_error( labels, shapes);
%
% [error_x, error_y] = cummErrorCurve(clnf_error);
%
% plot(error_x, error_y, 'DisplayName', 'CLNF wild', 'LineWidth',line_width);
% hold on;
plot(error_x, error_y, 'r', 'DisplayName', 'DCLM ', 'LineWidth',line_width);
load('../experiments_in_the_wild/results/results_wild_clnf_general_final_inner.mat');
labels = experiments.labels([1:60,62:64,66:end],:,detected);
shapes = experiments.shapes([1:60,62:64,66:end],:,detected);
labels = experiments.labels([1:60,62:64,66:end],:,:);
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:);
% center the pixel
shapes = shapes(18:end,:,:) + 0.5;
clnf_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(clnf_error);
[error_x, error_y] = cummErrorCurve(clnf_error(inds));
plot(error_x, error_y, 'DisplayName', 'CLNF (ECCV 14)', 'LineWidth',line_width);
plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
hold on;
load('../experiments_in_the_wild/results/results_wild_dclm_general.mat');
labels = experiments.labels([1:60,62:64,66:end],:,detected);
shapes = experiments.shapes([1:60,62:64,66:end],:,detected);
labels = labels(18:end,:,:) - 1.0;
shapes = shapes(18:end,:,:);
dclm_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(dclm_error);
plot(error_x, error_y, 'r', 'DisplayName', 'DCLM ', 'LineWidth',line_width);
load('results/300W_pocr.mat');
% center the pixel
shapes_all = experiments.shapes;
pocr_error = compute_error(labels, shapes_all(:,:,detected));
pocr_error = compute_error(labels, shapes_all(:,:,:) + 1.0);
[error_x, error_y] = cummErrorCurve(pocr_error);
[error_x, error_y] = cummErrorCurve(pocr_error(inds));
plot(error_x, error_y, 'DisplayName', 'PO-CR (CVPR 15)', 'LineWidth',line_width);
plot(error_x, error_y, 'DisplayName', 'PO-CR', 'LineWidth',line_width);
hold on;
load('results/intraface_wild_resize.mat');
labels_all = labels_all(18:end,:,detected);
load('results/300W-CFSS.mat');
% center the pixel
shapes_all = shapes_all(18:end,:,detected) + 0.5;
shapes_all = zeros(68, 2, size(estimatedPose,1));
for i = 1:size(estimatedPose,1)
shapes_all(:,1,i) = estimatedPose(i,1:68)';
shapes_all(:,2,i) = estimatedPose(i,69:end)';
end
shapes_all = shapes_all([1:60,62:64,66:end],:,:);
shapes_all = shapes_all(18:end,:,:);
intraface_wild_error = compute_error(labels_all, shapes_all);
cfss_error = compute_error(labels, shapes_all(:,:,:) + 0.5);
[error_x, error_y] = cummErrorCurve(intraface_wild_error);
[error_x, error_y] = cummErrorCurve(cfss_error(inds));
plot(error_x, error_y, '.-g','DisplayName', 'SDM (CVPR 13)', 'LineWidth',line_width);
plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
hold on;
load('results/300W_sdm.mat');
% center the pixel
shapes_all = experiments.shapes;
sdm_error = compute_error(labels, shapes_all(:,:,:));
[error_x, error_y] = cummErrorCurve(sdm_error(inds));
plot(error_x, error_y, 'DisplayName', 'SDM (CVPR 13)', 'LineWidth',line_width);
hold on;
load('results/GNDPM_300W.mat');
% center the pixel
shapes_all = shapes_all(:,:,detected) + 1;
labels_all = labels_all(:,:,detected);
shapes_all = shapes_all(:,:,:) + 1;
labels_all = labels_all(:,:,:);
gndpm_wild_error = compute_error(labels_all, shapes_all);
[error_x, error_y] = cummErrorCurve(gndpm_wild_error);
[error_x, error_y] = cummErrorCurve(gndpm_wild_error(inds));
plot(error_x, error_y, '-.','DisplayName', 'GNDPM (CVPR 14)', 'LineWidth',line_width);
plot(error_x, error_y, 'DisplayName', 'GNDPM', 'LineWidth',line_width);
hold on;
load('results/zhu_wild.mat');
labels_all = labels_all(18:end,:,detected);
shapes_all = shapes_all(18:end,:,detected);
labels_all = labels_all(18:end,:,:);
shapes_all = shapes_all(18:end,:,:);
zhu_wild_error = compute_error(labels_all, shapes_all);
[error_x, error_y] = cummErrorCurve(zhu_wild_error);
[error_x, error_y] = cummErrorCurve(zhu_wild_error(inds));
plot(error_x, error_y, '.-c','DisplayName', 'Tree based (CVPR 12)', 'LineWidth',line_width);
plot(error_x, error_y, 'c','DisplayName', 'Tree based', 'LineWidth',line_width);
load('results/results_wild_clm.mat');
labels = experiments.labels([1:60,62:64,66:end],:,detected);
shapes = experiments.shapes([1:60,62:64,66:end],:,detected);
labels = labels(18:end,:,:);
% center the pixel
shapes = shapes(18:end,:,:) + 0.5;
clm_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(clm_error);
plot(error_x, error_y, '--b','DisplayName', 'CLM+', 'LineWidth',line_width);
% load('results/results_wild_clm.mat');
% labels = experiments.labels([1:60,62:64,66:end],:,:);
% shapes = experiments.shapes([1:60,62:64,66:end],:,:);
% labels = labels(18:end,:,:);
% % center the pixel
% shapes = shapes(18:end,:,:) + 0.5;
%
% clm_error = compute_error( labels, shapes);
%
% [error_x, error_y] = cummErrorCurve(clm_error);
%
% plot(error_x, error_y, '--b','DisplayName', 'CLM+', 'LineWidth',line_width);
load('results/drmf_wild.mat');
labels_all = labels_all(18:end,:,detected);
shapes_all = shapes_all(18:end,:,detected);
labels_all = labels_all(18:end,:,:);
shapes_all = shapes_all(18:end,:,:);
drmf_error = compute_error(labels_all, shapes_all);
[error_x, error_y] = cummErrorCurve(drmf_error);
[error_x, error_y] = cummErrorCurve(drmf_error(inds));
plot(error_x, error_y, '-.k','DisplayName', 'DRMF (CVPR 13)', 'LineWidth',line_width);
plot(error_x, error_y, 'k','DisplayName', 'DRMF (CVPR 13)', 'LineWidth',line_width);
set(gca,'xtick',[0:0.05:0.15])
xlim([0,0.08]);
xlabel('Size normalised shape RMS error','FontName','Helvetica');
set(gca,'xtick',[0:0.01:0.1])
xlim([0.01,0.07]);
xlabel('IOD normalised MSE','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
% title('Fitting in the wild without outline','FontSize',60,'FontName','Helvetica');
leg = legend('show', 'Location', 'SouthEast');
set(leg,'FontSize',30)
savefig('results/dclm-in-the-wild-no-outline.fig');
print -dpdf results/dclm-in-the-wild-clnf-no-outline.pdf
print -dpng results/dclm-300W-no-outline.png
%%
@@ -160,18 +155,6 @@ axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 6;
hold on;
load('../experiments_in_the_wild/results/results_wild_clnf_general_final_inner.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
% center the pixel
shapes = experiments.shapes([1:60,62:64,66:end],:,:) + 1;
clnf_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(clnf_error);
hold on;
plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
load('../experiments_in_the_wild/results/results_wild_dclm_general.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
% center the pixel
@@ -179,50 +162,79 @@ shapes = experiments.shapes([1:60,62:64,66:end],:,:) + 0.5;
dclm_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(dclm_error);
[error_x, error_y] = cummErrorCurve(dclm_error(inds));
plot(error_x, error_y, 'r', 'DisplayName', 'DCLM ', 'LineWidth',line_width);
load('../experiments_in_the_wild/results/results_wild_clnf_general_final_inner.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
% center the pixel
shapes = experiments.shapes([1:60,62:64,66:end],:,:) + 1;
clnf_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(clnf_error(inds));
hold on;
plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
load('results/300W-CFSS.mat');
% center the pixel
shapes_all = zeros(68, 2, size(estimatedPose,1));
for i = 1:size(estimatedPose,1)
shapes_all(:,1,i) = estimatedPose(i,1:68)';
shapes_all(:,2,i) = estimatedPose(i,69:end)';
end
cfss_error = compute_error(labels, shapes_all(:,:,:) + 0.5);
[error_x, error_y] = cummErrorCurve(cfss_error(inds));
plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
load('results/zhu_wild.mat');
zhu_wild_error = compute_error(labels_all(:,:,:), shapes_all(:,:,:));
[error_x, error_y] = cummErrorCurve(zhu_wild_error);
plot(error_x, error_y, '.-c','DisplayName', 'Zhu et al.', 'LineWidth',line_width);
plot(error_x, error_y, '.-c','DisplayName', 'Tree based', 'LineWidth',line_width);
load('results/yu_wild.mat');
% load('results/yu_wild.mat');
yu_wild_error = compute_error(lmark_dets_all(:,:,:)-1, shapes_all(:,:,:));
yu_wild_error(isnan(yu_wild_error)) = 1;
yu_wild_error(isinf(yu_wild_error)) = 1;
% yu_wild_error = compute_error(lmark_dets_all(:,:,:)-1, shapes_all(:,:,:));
% yu_wild_error(isnan(yu_wild_error)) = 1;
% yu_wild_error(isinf(yu_wild_error)) = 1;
%
% [error_x, error_y] = cummErrorCurve(yu_wild_error(inds));
%
% plot(error_x, error_y, 'xg','DisplayName', 'Yu et al.', 'LineWidth',line_width);
[error_x, error_y] = cummErrorCurve(yu_wild_error);
plot(error_x, error_y, 'xg','DisplayName', 'Yu et al.', 'LineWidth',line_width);
load('results/results_wild_clm.mat');
experiments(1).labels = experiments(1).labels([1:60,62:64,66:end],:,:);
% center the pixel
experiments(1).shapes = experiments(1).shapes([1:60,62:64,66:end],:,:) + 0.5;
clm_error = compute_error( experiments(1).labels, experiments(1).shapes);
[error_x, error_y] = cummErrorCurve(clm_error);
plot(error_x, error_y, '--b','DisplayName', 'CLM+', 'LineWidth',line_width);
% load('results/results_wild_clm.mat');
% experiments(1).labels = experiments(1).labels([1:60,62:64,66:end],:,:);
% % center the pixel
% experiments(1).shapes = experiments(1).shapes([1:60,62:64,66:end],:,:) + 0.5;
%
% clm_error = compute_error( experiments(1).labels, experiments(1).shapes);
%
% [error_x, error_y] = cummErrorCurve(clm_error(inds));
%
% plot(error_x, error_y, '--b','DisplayName', 'CLM+', 'LineWidth',line_width);
load('results/drmf_wild.mat');
drmf_error = compute_error(labels_all, shapes_all);
[error_x, error_y] = cummErrorCurve(drmf_error);
[error_x, error_y] = cummErrorCurve(drmf_error(inds));
plot(error_x, error_y, '-.k','DisplayName', 'DRMF', 'LineWidth',line_width);
plot(error_x, error_y, 'k','DisplayName', 'DRMF', 'LineWidth',line_width);
set(gca,'xtick',[0:0.05:0.15])
xlim([0,0.15]);
xlabel('Size normalised shape RMS error','FontName','Helvetica');
set(gca,'xtick',[0.01:0.02:0.15])
xlim([0.01,0.15]);
xlabel('IOD normalised MSE','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
%title('Fitting in the wild','FontSize',60,'FontName','Helvetica');
@@ -230,4 +242,5 @@ grid on
legend('show', 'Location', 'SouthEast');
print -dpdf results/dclm-in-the-wild-comparison.pdf
savefig('results/dclm-in-the-wild-outline.fig');
print -dpng results/dclm-300W-outline.png

258
matlab_version/experiments_iccv_300w/Display_dlm_results_per_db.m Normal file
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@@ -0,0 +1,258 @@
clear
%%
inds = 1:337;
scrsz = get(0,'ScreenSize');
figure1 = figure('Position',[20 50 3*scrsz(3)/4 0.9*scrsz(4)]);
set(figure1,'Units','Inches');
pos = get(figure1,'Position');
set(figure1,'PaperPositionMode','Auto','PaperUnits','Inches','PaperSize',[pos(3), pos(4)])
% Create axes
axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 6;
hold on;
load('results/results_wild_clnf.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:);
shapes = shapes(18:end,:,:);
clnf_error = compute_error( labels(:,:,inds), shapes(:,:,inds));
[error_x, error_y] = cummErrorCurve(clnf_error);
hold on;
plot(error_x, error_y, 'r','DisplayName', 'CLM + CCNF', 'LineWidth',line_width);
load('results/intraface_wild_resize.mat');
labels_all = labels_all(18:end,:,:);
shapes_all = shapes_all(18:end,:,:);
intraface_wild_error = compute_error(labels_all(:,:,inds), shapes_all(:,:,inds));
[error_x, error_y] = cummErrorCurve(intraface_wild_error);
plot(error_x, error_y, '.-g','DisplayName', 'SDM [32]', 'LineWidth',line_width);
load('results/zhu_wild.mat');
labels_all = labels_all(18:end,:,:);
shapes_all = shapes_all(18:end,:,:);
zhu_wild_error = compute_error(labels_all(:,:,inds), shapes_all(:,:,inds));
[error_x, error_y] = cummErrorCurve(zhu_wild_error);
plot(error_x, error_y, '.-c','DisplayName', 'Tree based (p204) [28]', 'LineWidth',line_width);
load('results/results_wild_clm.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:);
shapes = shapes(18:end,:,:);
clm_error = compute_error( labels(:,:,inds), shapes(:,:,inds));
[error_x, error_y] = cummErrorCurve(clm_error);
plot(error_x, error_y, '--b','DisplayName', 'CLM + SVR', 'LineWidth',line_width);
load('results/drmf_wild.mat');
labels_all = labels_all(18:end,:,:);
shapes_all = shapes_all(18:end,:,:);
drmf_error = compute_error(labels_all(:,:,inds), shapes_all(:,:,inds));
[error_x, error_y] = cummErrorCurve(drmf_error);
plot(error_x, error_y, '-.k','DisplayName', 'DRMF [27]', 'LineWidth',line_width);
set(gca,'xtick',[0:0.05:0.15])
xlim([0,0.15]);
xlabel('Size normalised shape RMS error','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
% title('Fitting in the wild without outline','FontSize',60,'FontName','Helvetica');
legend('show', 'Location', 'SouthEast');
print -dpdf results/in-the-wild-clnf-no-outline_afw.pdf
%%
inds = 562:696;
scrsz = get(0,'ScreenSize');
figure1 = figure('Position',[20 50 3*scrsz(3)/4 0.9*scrsz(4)]);
set(figure1,'Units','Inches');
pos = get(figure1,'Position');
set(figure1,'PaperPositionMode','Auto','PaperUnits','Inches','PaperSize',[pos(3), pos(4)])
% Create axes
axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 6;
hold on;
load('results/results_wild_clnf.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:);
shapes = shapes(18:end,:,:);
clnf_error = compute_error( labels(:,:,inds), shapes(:,:,inds));
[error_x, error_y] = cummErrorCurve(clnf_error);
hold on;
plot(error_x, error_y, 'r','DisplayName', 'CLM + CCNF', 'LineWidth',line_width);
load('../experiments_in_the_wild/results/results_wild_dclm_general.mat');
labels = experiments.labels([1:60,62:64,66:end],:,inds);
shapes = experiments.shapes([1:60,62:64,66:end],:,inds);
labels = labels(18:end,:,:);
shapes = shapes(18:end,:,:) + 1.0;
dclm_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(dclm_error);
plot(error_x, error_y, 'DisplayName', 'DCLM ', 'LineWidth',line_width);
load('results/intraface_wild_resize.mat');
labels_all = labels_all(18:end,:,:);
shapes_all = shapes_all(18:end,:,:);
intraface_wild_error = compute_error(labels_all(:,:,inds), shapes_all(:,:,inds));
[error_x, error_y] = cummErrorCurve(intraface_wild_error);
plot(error_x, error_y, '.-g','DisplayName', 'SDM [32]', 'LineWidth',line_width);
load('results/zhu_wild.mat');
labels_all = labels_all(18:end,:,:);
shapes_all = shapes_all(18:end,:,:);
zhu_wild_error = compute_error(labels_all(:,:,inds), shapes_all(:,:,inds));
[error_x, error_y] = cummErrorCurve(zhu_wild_error);
plot(error_x, error_y, '.-c','DisplayName', 'Tree based (p204) [28]', 'LineWidth',line_width);
load('results/results_wild_clm.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:);
shapes = shapes(18:end,:,:);
clm_error = compute_error( labels(:,:,inds), shapes(:,:,inds));
[error_x, error_y] = cummErrorCurve(clm_error);
plot(error_x, error_y, '--b','DisplayName', 'CLM + SVR', 'LineWidth',line_width);
load('results/drmf_wild.mat');
labels_all = labels_all(18:end,:,:);
shapes_all = shapes_all(18:end,:,:);
drmf_error = compute_error(labels_all(:,:,inds), shapes_all(:,:,inds));
[error_x, error_y] = cummErrorCurve(drmf_error);
plot(error_x, error_y, '-.k','DisplayName', 'DRMF [27]', 'LineWidth',line_width);
set(gca,'xtick',[0:0.05:0.15])
xlim([0,0.15]);
xlabel('Size normalised shape RMS error','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
% title('Fitting in the wild without outline','FontSize',60,'FontName','Helvetica');
legend('show', 'Location', 'SouthEast');
print -dpdf results/in-the-wild-clnf-no-outline_ibug.pdf
%%
inds = [338:561,697:1026];
scrsz = get(0,'ScreenSize');
figure1 = figure('Position',[20 50 3*scrsz(3)/4 0.9*scrsz(4)]);
set(figure1,'Units','Inches');
pos = get(figure1,'Position');
set(figure1,'PaperPositionMode','Auto','PaperUnits','Inches','PaperSize',[pos(3), pos(4)])
% Create axes
axes1 = axes('Parent',figure1,'FontSize',40,'FontName','Helvetica');
line_width = 6;
hold on;
load('results/results_wild_clnf.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:);
shapes = shapes(18:end,:,:);
clnf_error = compute_error( labels(:,:,inds), shapes(:,:,inds));
[error_x, error_y] = cummErrorCurve(clnf_error);
hold on;
plot(error_x, error_y, 'r','DisplayName', 'CLM + CCNF', 'LineWidth',line_width);
load('results/intraface_wild_resize.mat');
labels_all = labels_all(18:end,:,:);
shapes_all = shapes_all(18:end,:,:);
intraface_wild_error = compute_error(labels_all(:,:,inds), shapes_all(:,:,inds));
[error_x, error_y] = cummErrorCurve(intraface_wild_error);
plot(error_x, error_y, '.-g','DisplayName', 'SDM [32]', 'LineWidth',line_width);
load('results/zhu_wild.mat');
labels_all = labels_all(18:end,:,:);
shapes_all = shapes_all(18:end,:,:);
zhu_wild_error = compute_error(labels_all(:,:,inds), shapes_all(:,:,inds));
[error_x, error_y] = cummErrorCurve(zhu_wild_error);
plot(error_x, error_y, '.-c','DisplayName', 'Tree based (p204) [28]', 'LineWidth',line_width);
load('results/results_wild_clm.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:);
shapes = shapes(18:end,:,:);
clm_error = compute_error( labels(:,:,inds), shapes(:,:,inds));
[error_x, error_y] = cummErrorCurve(clm_error);
plot(error_x, error_y, '--b','DisplayName', 'CLM + SVR', 'LineWidth',line_width);
load('results/drmf_wild.mat');
labels_all = labels_all(18:end,:,:);
shapes_all = shapes_all(18:end,:,:);
drmf_error = compute_error(labels_all(:,:,inds), shapes_all(:,:,inds));
[error_x, error_y] = cummErrorCurve(drmf_error);
plot(error_x, error_y, '-.k','DisplayName', 'DRMF [27]', 'LineWidth',line_width);
set(gca,'xtick',[0:0.05:0.15])
xlim([0,0.15]);
xlabel('Size normalised shape RMS error','FontName','Helvetica');
ylabel('Proportion of images','FontName','Helvetica');
grid on
% title('Fitting in the wild without outline','FontSize',60,'FontName','Helvetica');
legend('show', 'Location', 'SouthEast');
print -dpdf results/in-the-wild-clnf-no-outline_lfpw_helen.pdf

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matlab_version/experiments_iccv_300w/Script_CFSS_vis.m Normal file
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function Script_CFSS_vis()
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 = 'D:/Dropbox/Dropbox/AAM/test data/';
end
[images, detections, labels] = Collect_wild_imgs(root_test_data);
images = images(:);
labels = labels(:,:,:);
f = figure;
load('results/300W-CFSS.mat');
output_root = './wild_fit_cfss/';
for i=1:numel(images)
image = imread(images(i).img);
image_orig = image;
pred = estimatedPose(i,:);
preds = cat(2, pred(1:68)', pred(69:end)');
actualShape = squeeze(labels(i,:,:));
v_points = sum(squeeze(labels(i,:,:)),2) > 0;
[height_img, width_img,~] = size(image_orig);
width = max(actualShape(v_points,1)) - min(actualShape(v_points,1));
height = max(actualShape(v_points,2)) - min(actualShape(v_points,2));
sz = (width+height)/2.0;
img_min_x = max(int32(min(actualShape(v_points,1))) - width/3,1);
img_max_x = min(int32(max(actualShape(v_points,1))) + width/3,width_img);
img_min_y = max(int32(min(actualShape(v_points,2))) - height/3,1);
img_max_y = min(int32(max(actualShape(v_points,2))) + height/3,height_img);
preds(:,1) = preds(:,1) - double(img_min_x);
preds(:,2) = preds(:,2) - double(img_min_y);
image_orig = image_orig(img_min_y:img_max_y, img_min_x:img_max_x, :);
scale = 600/sz;
image_orig = imresize(image_orig, scale);
% valid points to draw (not to draw
% occluded ones)
% f = figure('visible','off');
try
if(max(image_orig(:)) > 1)
imshow(double(image_orig)/255, 'Border', 'tight');
else
imshow(double(image_orig), 'Border', 'tight');
end
axis equal;
hold on;
plot(preds(:,1) * scale, preds(:,2) * scale,'.r','MarkerSize', int32(40));
plot(preds(:,1) * scale, preds(:,2) * scale,'.g','MarkerSize',int32(20));
% plot(preds(:,1), preds(:,2),'.w','MarkerSize',40);
% plot(preds(:,1), preds(:,2),'.k','MarkerSize',30);
print(f, '-r80', '-dpng', sprintf('%s/%s%d_cfss.png', output_root, 'fit', i));
% print(f, '-dpng', sprintf('%s/%s%d.png', output_root, 'fit', i));
% close(f);
hold off;
% drawnow expose
% close(f);
catch warn
end
end

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function Script_CLNF_vis()
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 = 'D:/Dropbox/Dropbox/AAM/test data/';
end
[images, detections, labels] = Collect_wild_imgs(root_test_data);
images = images(:);
labels = labels(:,:,:);
f = figure;
load('../experiments_in_the_wild/results/results_wild_clnf_general_final_inner.mat');
output_root = './wild_fit_clnf/';
for i=1:numel(images)
image = imread(images(i).img);
image_orig = image;
preds = experiments.shapes(:,:,i);
actualShape = squeeze(labels(i,:,:));
v_points = sum(squeeze(labels(i,:,:)),2) > 0;
[height_img, width_img,~] = size(image_orig);
width = max(actualShape(v_points,1)) - min(actualShape(v_points,1));
height = max(actualShape(v_points,2)) - min(actualShape(v_points,2));
sz = (width+height)/2.0;
img_min_x = max(int32(min(actualShape(v_points,1))) - width/3,1);
img_max_x = min(int32(max(actualShape(v_points,1))) + width/3,width_img);
img_min_y = max(int32(min(actualShape(v_points,2))) - height/3,1);
img_max_y = min(int32(max(actualShape(v_points,2))) + height/3,height_img);
preds(:,1) = preds(:,1) - double(img_min_x);
preds(:,2) = preds(:,2) - double(img_min_y);
image_orig = image_orig(img_min_y:img_max_y, img_min_x:img_max_x, :);
scale = 600/sz;
image_orig = imresize(image_orig, scale);
% valid points to draw (not to draw
% occluded ones)
% f = figure('visible','off');
try
if(max(image_orig(:)) > 1)
imshow(double(image_orig)/255, 'Border', 'tight');
else
imshow(double(image_orig), 'Border', 'tight');
end
axis equal;
hold on;
plot(preds(:,1) * scale, preds(:,2) * scale,'.r','MarkerSize', int32(40));
plot(preds(:,1) * scale, preds(:,2) * scale,'.g','MarkerSize',int32(20));
% plot(preds(:,1), preds(:,2),'.w','MarkerSize',40);
% plot(preds(:,1), preds(:,2),'.k','MarkerSize',30);
print(f, '-r80', '-dpng', sprintf('%s/%s%d_clnf.png', output_root, 'fit', i));
% print(f, '-dpng', sprintf('%s/%s%d.png', output_root, 'fit', i));
% close(f);
hold off;
% drawnow expose
% close(f);
catch warn
end
end

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matlab_version/experiments_iccv_300w/Script_DCLM_vis.m Normal file
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function Script_DCLM_vis()
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 = 'D:/Dropbox/Dropbox/AAM/test data/';
end
[images, detections, labels] = Collect_wild_imgs(root_test_data);
images = images(:);
labels = labels(:,:,:);
f = figure;
load('../experiments_in_the_wild/results/results_wild_dclm_general.mat');
output_root = './wild_fit_dclm/';
for i=1:numel(images)
image = imread(images(i).img);
image_orig = image;
preds = experiments.shapes(:,:,i);
actualShape = squeeze(labels(i,:,:));
v_points = sum(squeeze(labels(i,:,:)),2) > 0;
[height_img, width_img,~] = size(image_orig);
width = max(actualShape(v_points,1)) - min(actualShape(v_points,1));
height = max(actualShape(v_points,2)) - min(actualShape(v_points,2));
sz = (width+height)/2.0;
img_min_x = max(int32(min(actualShape(v_points,1))) - width/3,1);
img_max_x = min(int32(max(actualShape(v_points,1))) + width/3,width_img);
img_min_y = max(int32(min(actualShape(v_points,2))) - height/3,1);
img_max_y = min(int32(max(actualShape(v_points,2))) + height/3,height_img);
preds(:,1) = preds(:,1) - double(img_min_x);
preds(:,2) = preds(:,2) - double(img_min_y);
image_orig = image_orig(img_min_y:img_max_y, img_min_x:img_max_x, :);
scale = 600/sz;
image_orig = imresize(image_orig, scale);
% valid points to draw (not to draw
% occluded ones)
% f = figure('visible','off');
try
if(max(image_orig(:)) > 1)
imshow(double(image_orig)/255, 'Border', 'tight');
else
imshow(double(image_orig), 'Border', 'tight');
end
axis equal;
hold on;
plot(preds(:,1) * scale, preds(:,2) * scale,'.r','MarkerSize', int32(40));
plot(preds(:,1) * scale, preds(:,2) * scale,'.g','MarkerSize',int32(20));
% plot(preds(:,1), preds(:,2),'.w','MarkerSize',40);
% plot(preds(:,1), preds(:,2),'.k','MarkerSize',30);
print(f, '-r80', '-dpng', sprintf('%s/%s%d_dclm.png', output_root, 'fit', i));
% print(f, '-dpng', sprintf('%s/%s%d.png', output_root, 'fit', i));
% close(f);
hold off;
% drawnow expose
% close(f);
catch warn
end
end

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matlab_version/experiments_in_the_wild/Display_clnf_dclm_results.m
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@@ -39,6 +39,19 @@ clm_error = compute_error( labels, shapes);
plot(error_x, error_y, 'DisplayName', 'DCLM', 'LineWidth',line_width);
load('results/results_wild_dclm_wild.mat');
labels = experiments.labels([1:60,62:64,66:end],:,:);
shapes = experiments.shapes([1:60,62:64,66:end],:,:);
labels = labels(18:end,:,:) - 0.5;
shapes = shapes(18:end,:,:);
clm_error = compute_error( labels, shapes);
[error_x, error_y] = cummErrorCurve(clm_error);
plot(error_x, error_y, 'DisplayName', 'DCLM2', 'LineWidth',line_width);
set(gca,'xtick',[0:0.01:0.08])
xlim([0,0.08]);
xlabel('Size normalised shape RMS error','FontName','Helvetica');

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matlab_version/experiments_in_the_wild/Script_CLM_general_vis_resp.m Normal file
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function Script_CLM_general_vis_resp()
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 = 'D:/Dropbox/Dropbox/AAM/test data/';
end
[images, detections, labels] = Collect_wild_imgs(root_test_data);
to_vis = [29,51,75,79,81,9,112,146,152,172,199,204,230,234,235,241,251,256,263,272,279,340,342,348,358,362,394,478,484,486];
images = images(to_vis);
detections = detections(to_vis,:);
labels = labels(to_vis,:);
%% loading the patch experts
clmParams = struct;
clmParams.window_size = [31,31;];
clmParams.numPatchIters = size(clmParams.window_size,1);
[patches] = Load_Patch_Experts( '../models/general/', 'svr_patches_*_general.mat', [], [], clmParams);
%% Fitting the model to the provided image
out_dir_root = './patch_expert_responses/';
if(~exist(out_dir_root, 'dir'))
mkdir(out_dir_root);
end
% 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);
clmParams.startScale = 1;
clmParams.num_RLMS_iter = 10;
clmParams.fTol = 0.01;
clmParams.useMultiScale = true;
clmParams.use_multi_modal = 1;
clmParams.multi_modal_types = patches(1).multi_modal_types;
clmParams.numPatchIters = 1;
% for recording purposes
experiment.params = clmParams;
num_points = numel(M)/3;
for i=1:numel(images)
image = imread(images(i).img);
image_orig = image;
if(size(image,3) == 3)
image = rgb2gray(image);
end
bbox = detections(i,:);
out_dir = [out_dir_root, '/', num2str(to_vis(i))];
if(~exist(out_dir, 'dir'))
mkdir(out_dir);
end
Fitting_from_bb_vis(image, [], bbox, pdm, patches, clmParams, out_dir);
end
end

4
matlab_version/experiments_in_the_wild/Script_CLNF_general_no_out.m
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@@ -10,7 +10,7 @@ addpath('../models/');
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/';
root_test_data = 'D:/Dropbox/Dropbox/AAM/test data/';
end
[images, detections, labels] = Collect_wild_imgs(root_test_data);
@@ -26,7 +26,7 @@ clmParams.numPatchIters = size(clmParams.window_size,1);
%% Fitting the model to the provided image
verbose = false; % set to true to visualise the fitting
verbose = true; % set to true to visualise the fitting
output_root = './wild_fit_clnf/';
% the default PDM to use

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matlab_version/experiments_in_the_wild/Script_CLNF_general_vis_resp.m Normal file
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@@ -0,0 +1,82 @@
function Script_CLNF_general_vis_resp()
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 = 'D:/Dropbox/Dropbox/AAM/test data/';
end
[images, detections, labels] = Collect_wild_imgs(root_test_data);
to_vis = [29,51,75,79,81,9,112,146,152,172,199,204,230,234,235,241,251,256,263,272,279,340,342,348,358,362,394,478,484,486];
images = images(to_vis);
detections = detections(to_vis,:);
labels = labels(to_vis,:);
%% loading the patch experts
clmParams = struct;
clmParams.window_size = [31,31;];
clmParams.numPatchIters = size(clmParams.window_size,1);
[patches] = Load_Patch_Experts( '../models/general/', 'ccnf_patches_*_general.mat', [], [], clmParams);
%% Fitting the model to the provided image
out_dir_root = './patch_expert_responses/';
if(~exist(out_dir_root, 'dir'))
mkdir(out_dir_root);
end
% 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);
clmParams.startScale = 1;
clmParams.num_RLMS_iter = 10;
clmParams.fTol = 0.01;
clmParams.useMultiScale = true;
clmParams.use_multi_modal = 1;
clmParams.multi_modal_types = patches(1).multi_modal_types;
clmParams.numPatchIters = 1;
% for recording purposes
experiment.params = clmParams;
num_points = numel(M)/3;
for i=1:numel(images)
image = imread(images(i).img);
image_orig = image;
if(size(image,3) == 3)
image = rgb2gray(image);
end
bbox = detections(i,:);
out_dir = [out_dir_root, '/', num2str(to_vis(i))];
if(~exist(out_dir, 'dir'))
mkdir(out_dir);
end
Fitting_from_bb_vis(image, [], bbox, pdm, patches, clmParams, out_dir);
end
end

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function Script_DCLM_general_corrs()
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 = 'D:/Dropbox/Dropbox/AAM/test data/';
end
[images, detections, labels] = Collect_wild_imgs(root_test_data);
%% loading the patch experts
clmParams = struct;
clmParams.window_size = [25,25; 23,23; 21,21; 21,21];
clmParams.numPatchIters = size(clmParams.window_size,1);
[patches] = Load_DCLM_Patch_Experts( '../models/general/', 'dccnf_patches_*_general_corrs.mat', [], [], clmParams);
%% Fitting the model to the provided image
output_root = './wild_fit_dclm/';
% 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);
clmParams.regFactor = [35, 27, 20, 20];
clmParams.sigmaMeanShift = [1.25, 1.375, 1.5, 1.5];
clmParams.tikhonov_factor = [2.5, 5, 7.5, 7.5];
clmParams.startScale = 1;
clmParams.num_RLMS_iter = 10;
clmParams.fTol = 0.01;
clmParams.useMultiScale = true;
clmParams.use_multi_modal = 1;
clmParams.multi_modal_types = patches(1).multi_modal_types;
clmParams.numPatchIters = 4;
% for recording purposes
experiment.params = clmParams;
num_points = numel(M)/3;
shapes_all = zeros(size(labels,2),size(labels,3), size(labels,1));
labels_all = zeros(size(labels,2),size(labels,3), size(labels,1));
lhoods = zeros(numel(images),1);
all_lmark_lhoods = zeros(num_points, numel(images));
all_views_used = zeros(numel(images),1);
% Use the multi-hypothesis model, as bounding box tells nothing about
% orientation
multi_view = false;
verbose = false;
tic
for i=1:numel(images)
image = imread(images(i).img);
image_orig = image;
if(size(image,3) == 3)
image = rgb2gray(image);
end
bbox = detections(i,:);
% have a multi-view version
if(multi_view)
views = [0,0,0; 0,-30,0; -30,0,0; 0,30,0; 30,0,0];
views = views * pi/180;
shapes = zeros(num_points, 2, size(views,1));
ls = zeros(size(views,1),1);
lmark_lhoods = zeros(num_points,size(views,1));
views_used = zeros(num_points,size(views,1));
% Find the best orientation
for v = 1:size(views,1)
[shapes(:,:,v),~,~,ls(v),lmark_lhoods(:,v),views_used(v)] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams, 'orientation', views(v,:));
end
[lhood, v_ind] = max(ls);
lmark_lhood = lmark_lhoods(:,v_ind);
shape = shapes(:,:,v_ind);
view_used = views_used(v);
else
[shape,~,~,lhood,lmark_lhood,view_used] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams);
end
all_lmark_lhoods(:,i) = lmark_lhood;
all_views_used(i) = view_used;
shapes_all(:,:,i) = shape;
labels_all(:,:,i) = labels(i,:,:);
if(mod(i, 50)==0)
fprintf('%d done\n', i );
end
lhoods(i) = lhood;
if(verbose)
actualShape = squeeze(labels(i,:,:));
[height_img, width_img,~] = size(image_orig);
width = max(actualShape(:,1)) - min(actualShape(:,1));
height = max(actualShape(:,2)) - min(actualShape(:,2));
img_min_x = max(int32(min(actualShape(:,1))) - width/3,1);
img_max_x = min(int32(max(actualShape(:,1))) + width/3,width_img);
img_min_y = max(int32(min(actualShape(:,2))) - height/3,1);
img_max_y = min(int32(max(actualShape(:,2))) + height/3,height_img);
shape(:,1) = shape(:,1) - double(img_min_x);
shape(:,2) = shape(:,2) - double(img_min_y);
image_orig = image_orig(img_min_y:img_max_y, img_min_x:img_max_x, :);
% valid points to draw (not to draw
% occluded ones)
v_points = sum(squeeze(labels(i,:,:)),2) > 0;
% f = figure('visible','off');
f = figure;
try
if(max(image_orig(:)) > 1)
imshow(double(image_orig)/255, 'Border', 'tight');
else
imshow(double(image_orig), 'Border', 'tight');
end
axis equal;
hold on;
plot(shape(v_points,1), shape(v_points,2),'.r','MarkerSize',20);
plot(shape(v_points,1), shape(v_points,2),'.b','MarkerSize',10);
% print(f, '-r80', '-dpng', sprintf('%s/%s%d.png', output_root, 'fit', i));
% print(f, '-djpeg', sprintf('%s/%s%d.jpg', output_root, 'fit', i));
% close(f);
hold off;
close(f);
catch warn
end
end
end
toc
experiment.errors_normed = compute_error(labels_all - 0.5, shapes_all);
experiment.lhoods = lhoods;
experiment.shapes = shapes_all;
experiment.labels = labels_all;
experiment.all_lmark_lhoods = all_lmark_lhoods;
experiment.all_views_used = all_views_used;
% save the experiment
if(~exist('experiments', 'var'))
experiments = experiment;
else
experiments = cat(1, experiments, experiment);
end
fprintf('experiment %d done: mean normed error %.3f median normed error %.4f\n', ...
numel(experiments), mean(experiment.errors_normed), median(experiment.errors_normed));
%%
output_results = 'results/results_wild_dclm_general_corr.mat';
save(output_results, 'experiments');
end

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function Script_DCLM_general_rmses()
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 = 'D:/Dropbox/Dropbox/AAM/test data/';
end
[images, detections, labels] = Collect_wild_imgs(root_test_data);
%% loading the patch experts
clmParams = struct;
clmParams.window_size = [25,25; 23,23; 21,21; 21,21];
clmParams.numPatchIters = size(clmParams.window_size,1);
[patches] = Load_DCLM_Patch_Experts( '../models/general/', 'dccnf_patches_*_general_rmses.mat', [], [], clmParams);
%% Fitting the model to the provided image
output_root = './wild_fit_dclm/';
% 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);
clmParams.regFactor = [35, 27, 20, 20];
clmParams.sigmaMeanShift = [1.25, 1.375, 1.5, 1.5];
clmParams.tikhonov_factor = [2.5, 5, 7.5, 7.5];
clmParams.startScale = 1;
clmParams.num_RLMS_iter = 10;
clmParams.fTol = 0.01;
clmParams.useMultiScale = true;
clmParams.use_multi_modal = 1;
clmParams.multi_modal_types = patches(1).multi_modal_types;
clmParams.numPatchIters = 4;
% for recording purposes
experiment.params = clmParams;
num_points = numel(M)/3;
shapes_all = zeros(size(labels,2),size(labels,3), size(labels,1));
labels_all = zeros(size(labels,2),size(labels,3), size(labels,1));
lhoods = zeros(numel(images),1);
all_lmark_lhoods = zeros(num_points, numel(images));
all_views_used = zeros(numel(images),1);
% Use the multi-hypothesis model, as bounding box tells nothing about
% orientation
multi_view = false;
verbose = false;
tic
for i=1:numel(images)
image = imread(images(i).img);
image_orig = image;
if(size(image,3) == 3)
image = rgb2gray(image);
end
bbox = detections(i,:);
% have a multi-view version
if(multi_view)
views = [0,0,0; 0,-30,0; -30,0,0; 0,30,0; 30,0,0];
views = views * pi/180;
shapes = zeros(num_points, 2, size(views,1));
ls = zeros(size(views,1),1);
lmark_lhoods = zeros(num_points,size(views,1));
views_used = zeros(num_points,size(views,1));
% Find the best orientation
for v = 1:size(views,1)
[shapes(:,:,v),~,~,ls(v),lmark_lhoods(:,v),views_used(v)] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams, 'orientation', views(v,:));
end
[lhood, v_ind] = max(ls);
lmark_lhood = lmark_lhoods(:,v_ind);
shape = shapes(:,:,v_ind);
view_used = views_used(v);
else
[shape,~,~,lhood,lmark_lhood,view_used] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams);
end
all_lmark_lhoods(:,i) = lmark_lhood;
all_views_used(i) = view_used;
shapes_all(:,:,i) = shape;
labels_all(:,:,i) = labels(i,:,:);
if(mod(i, 50)==0)
fprintf('%d done\n', i );
end
lhoods(i) = lhood;
if(verbose)
actualShape = squeeze(labels(i,:,:));
[height_img, width_img,~] = size(image_orig);
width = max(actualShape(:,1)) - min(actualShape(:,1));
height = max(actualShape(:,2)) - min(actualShape(:,2));
img_min_x = max(int32(min(actualShape(:,1))) - width/3,1);
img_max_x = min(int32(max(actualShape(:,1))) + width/3,width_img);
img_min_y = max(int32(min(actualShape(:,2))) - height/3,1);
img_max_y = min(int32(max(actualShape(:,2))) + height/3,height_img);
shape(:,1) = shape(:,1) - double(img_min_x);
shape(:,2) = shape(:,2) - double(img_min_y);
image_orig = image_orig(img_min_y:img_max_y, img_min_x:img_max_x, :);
% valid points to draw (not to draw
% occluded ones)
v_points = sum(squeeze(labels(i,:,:)),2) > 0;
% f = figure('visible','off');
f = figure;
try
if(max(image_orig(:)) > 1)
imshow(double(image_orig)/255, 'Border', 'tight');
else
imshow(double(image_orig), 'Border', 'tight');
end
axis equal;
hold on;
plot(shape(v_points,1), shape(v_points,2),'.r','MarkerSize',20);
plot(shape(v_points,1), shape(v_points,2),'.b','MarkerSize',10);
% print(f, '-r80', '-dpng', sprintf('%s/%s%d.png', output_root, 'fit', i));
% print(f, '-djpeg', sprintf('%s/%s%d.jpg', output_root, 'fit', i));
% close(f);
hold off;
close(f);
catch warn
end
end
end
toc
experiment.errors_normed = compute_error(labels_all - 0.5, shapes_all);
experiment.lhoods = lhoods;
experiment.shapes = shapes_all;
experiment.labels = labels_all;
experiment.all_lmark_lhoods = all_lmark_lhoods;
experiment.all_views_used = all_views_used;
% save the experiment
if(~exist('experiments', 'var'))
experiments = experiment;
else
experiments = cat(1, experiments, experiment);
end
fprintf('experiment %d done: mean normed error %.3f median normed error %.4f\n', ...
numel(experiments), mean(experiment.errors_normed), median(experiment.errors_normed));
%%
output_results = 'results/results_wild_dclm_general_rmse.mat';
save(output_results, 'experiments');
end

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function Script_DCLM_general_vis_resp()
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 = 'D:/Dropbox/Dropbox/AAM/test data/';
end
[images, detections, labels] = Collect_wild_imgs(root_test_data);
to_vis = [29,51,75,79,81,9,112,146,152,172,199,204,230,234,235,241,251,256,263,272,279,340,342,348,358,362,394,478,484,486];
images = images(to_vis);
detections = detections(to_vis,:);
labels = labels(to_vis,:);
%% loading the patch experts
clmParams = struct;
clmParams.window_size = [31,31;];
clmParams.numPatchIters = size(clmParams.window_size,1);
[patches] = Load_DCLM_Patch_Experts( '../models/general/', 'dccnf_patches_*_general.mat', [], [], clmParams);
%% Fitting the model to the provided image
out_dir_root = './patch_expert_responses/';
if(~exist(out_dir_root, 'dir'))
mkdir(out_dir_root);
end
% 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);
clmParams.startScale = 1;
clmParams.num_RLMS_iter = 10;
clmParams.fTol = 0.01;
clmParams.useMultiScale = true;
clmParams.use_multi_modal = 1;
clmParams.multi_modal_types = patches(1).multi_modal_types;
clmParams.numPatchIters = 1;
% for recording purposes
experiment.params = clmParams;
num_points = numel(M)/3;
for i=1:numel(images)
image = imread(images(i).img);
image_orig = image;
if(size(image,3) == 3)
image = rgb2gray(image);
end
bbox = detections(i,:);
out_dir = [out_dir_root, '/', num2str(to_vis(i))];
if(~exist(out_dir, 'dir'))
mkdir(out_dir);
end
Fitting_from_bb_vis(image, [], bbox, pdm, patches, clmParams, out_dir);
end
end

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function Script_DCLM_general_wild()
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 = 'D:/Dropbox/Dropbox/AAM/test data/';
end
[images, detections, labels] = Collect_wild_imgs(root_test_data);
%% loading the patch experts
clmParams = struct;
clmParams.window_size = [25,25; 23,23; 21,21; 21,21];
clmParams.numPatchIters = size(clmParams.window_size,1);
[patches] = Load_DCLM_Patch_Experts( '../models/wild/', 'dccnf_patches_*_wild.mat', [], [], clmParams);
%% Fitting the model to the provided image
output_root = './wild_fit_dclm/';
% 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);
clmParams.regFactor = [35, 27, 20, 20];
clmParams.sigmaMeanShift = [1.25, 1.375, 1.5, 1.5];
clmParams.tikhonov_factor = [2.5, 5, 7.5, 7.5];
clmParams.startScale = 1;
clmParams.num_RLMS_iter = 10;
clmParams.fTol = 0.01;
clmParams.useMultiScale = true;
clmParams.use_multi_modal = 1;
clmParams.multi_modal_types = patches(1).multi_modal_types;
clmParams.numPatchIters = 4;
% for recording purposes
experiment.params = clmParams;
num_points = numel(M)/3;
shapes_all = zeros(size(labels,2),size(labels,3), size(labels,1));
labels_all = zeros(size(labels,2),size(labels,3), size(labels,1));
lhoods = zeros(numel(images),1);
all_lmark_lhoods = zeros(num_points, numel(images));
all_views_used = zeros(numel(images),1);
% Use the multi-hypothesis model, as bounding box tells nothing about
% orientation
multi_view = true;
verbose = true;
tic
for i=1:numel(images)
image = imread(images(i).img);
image_orig = image;
if(size(image,3) == 3)
image = rgb2gray(image);
end
bbox = detections(i,:);
% have a multi-view version
if(multi_view)
views = [0,0,0; 0,-30,0; -30,0,0; 0,30,0; 30,0,0];
views = views * pi/180;
shapes = zeros(num_points, 2, size(views,1));
ls = zeros(size(views,1),1);
lmark_lhoods = zeros(num_points,size(views,1));
views_used = zeros(num_points,size(views,1));
% Find the best orientation
for v = 1:size(views,1)
[shapes(:,:,v),~,~,ls(v),lmark_lhoods(:,v),views_used(v)] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams, 'orientation', views(v,:));
end
[lhood, v_ind] = max(ls);
lmark_lhood = lmark_lhoods(:,v_ind);
shape = shapes(:,:,v_ind);
view_used = views_used(v);
else
[shape,~,~,lhood,lmark_lhood,view_used] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams);
end
all_lmark_lhoods(:,i) = lmark_lhood;
all_views_used(i) = view_used;
shapes_all(:,:,i) = shape;
labels_all(:,:,i) = labels(i,:,:);
if(mod(i, 200)==0)
fprintf('%d done\n', i );
end
lhoods(i) = lhood;
if(verbose)
actualShape = squeeze(labels(i,:,:));
[height_img, width_img,~] = size(image_orig);
width = max(actualShape(:,1)) - min(actualShape(:,1));
height = max(actualShape(:,2)) - min(actualShape(:,2));
img_min_x = max(int32(min(actualShape(:,1))) - width/3,1);
img_max_x = min(int32(max(actualShape(:,1))) + width/3,width_img);
img_min_y = max(int32(min(actualShape(:,2))) - height/3,1);
img_max_y = min(int32(max(actualShape(:,2))) + height/3,height_img);
shape(:,1) = shape(:,1) - double(img_min_x);
shape(:,2) = shape(:,2) - double(img_min_y);
image_orig = image_orig(img_min_y:img_max_y, img_min_x:img_max_x, :);
% valid points to draw (not to draw
% occluded ones)
v_points = sum(squeeze(labels(i,:,:)),2) > 0;
% f = figure('visible','off');
f = figure;
try
if(max(image_orig(:)) > 1)
imshow(double(image_orig)/255, 'Border', 'tight');
else
imshow(double(image_orig), 'Border', 'tight');
end
axis equal;
hold on;
plot(shape(v_points,1), shape(v_points,2),'.r','MarkerSize',20);
plot(shape(v_points,1), shape(v_points,2),'.b','MarkerSize',10);
% print(f, '-r80', '-dpng', sprintf('%s/%s%d.png', output_root, 'fit', i));
print(f, '-djpeg', sprintf('%s/%s%d.jpg', output_root, 'fit', i));
% close(f);
hold off;
close(f);
catch warn
end
end
end
toc
experiment.errors_normed = compute_error(labels_all - 0.5, shapes_all);
experiment.lhoods = lhoods;
experiment.shapes = shapes_all;
experiment.labels = labels_all;
experiment.all_lmark_lhoods = all_lmark_lhoods;
experiment.all_views_used = all_views_used;
% save the experiment
if(~exist('experiments', 'var'))
experiments = experiment;
else
experiments = cat(1, experiments, experiment);
end
fprintf('experiment %d done: mean normed error %.3f median normed error %.4f\n', ...
numel(experiments), mean(experiment.errors_normed), median(experiment.errors_normed));
%%
output_results = 'results/results_wild_dclm_wild.mat';
save(output_results, 'experiments');
end

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matlab_version/fitting/Fitting_from_bb.m
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@@ -140,6 +140,7 @@ function [ shape2D, global_params, local_params, final_lhood, landmark_lhoods, v
responses = PatchResponseDNN( patches, patchExperts(scale).patch_experts(view,:), visibilities(view,:), patchExperts(scale), clmParams.window_size(i,:));
end
% If a depth image is provided compute patch experts around it as
% well (unless it's the final iteration)
if(~isempty(DepthImage) && (i ~= clmParams.numPatchIters))

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matlab_version/fitting/Fitting_from_bb_vis.m Normal file
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@@ -0,0 +1,163 @@
function Fitting_from_bb_vis( Image, DepthImage, bounding_box, PDM, patchExperts, clmParams, out_dir, varargin)
%FITTING Summary of this function goes here
% Detailed explanation goes here
% the bounding box format is [minX, minY, maxX, maxY];
% the mean model shape
M = PDM.M;
num_points = numel(M) / 3;
if(any(strcmp(varargin,'orientation')))
orientation = varargin{find(strcmp(varargin, 'orientation'))+1};
rot = Euler2Rot(orientation);
else
rot = eye(3);
orientation = [0;0;0];
end
rot_m = rot * reshape(M, num_points, 3)';
width_model = max(rot_m(1,:)) - min(rot_m(1,:));
height_model = max(rot_m(2,:)) - min(rot_m(2,:));
a = (((bounding_box(3) - bounding_box(1)) / width_model) + ((bounding_box(4) - bounding_box(2))/ height_model)) / 2;
tx = (bounding_box(3) + bounding_box(1))/2;
ty = (bounding_box(4) + bounding_box(2))/2;
% correct it so that the bounding box is just around the minimum
% and maximum point in the initialised face
tx = tx - a*(min(rot_m(1,:)) + max(rot_m(1,:)))/2;
ty = ty - a*(min(rot_m(2,:)) + max(rot_m(2,:)))/2;
% visualisation of the initial state
%hold off;imshow(Image);hold on;plot(a*rot_m(1,:)+tx, a*rot_m(2,:)+ty,'.r');hold on;rectangle('Position', [bounding_box(1), bounding_box(2), bounding_box(3)-bounding_box(1), bounding_box(4)-bounding_box(2)]);
global_params = [a, 0, 0, 0, tx, ty]';
global_params(2:4) = orientation;
local_params = zeros(numel(PDM.E), 1);
if(any(strcmp(varargin,'gparam')))
global_params = varargin{find(strcmp(varargin, 'gparam'))+1};
end
if(any(strcmp(varargin,'lparam')))
local_params = varargin{find(strcmp(varargin, 'lparam'))+1};
end
scale = clmParams.startScale;
if(size(Image, 3) == 1)
GrayImage = Image;
else
GrayImage = rgb2gray(Image);
end
[heightImg, widthImg] = size(GrayImage);
% Some predefinitions for faster patch extraction
[xi, yi] = meshgrid(0:widthImg-1,0:heightImg-1);
xi = double(xi);
yi = double(yi);
GrayImageDb = double(GrayImage);
% multi iteration refinement using NU-RLMS in each one
i=1;
current_patch_scaling = patchExperts(scale).trainingScale;
visibilities = patchExperts(scale).visibilities;
view = GetView(patchExperts(scale).centers, global_params(2:4));
% The shape fitting is performed in the reference frame of the
% patch training scale
refGlobal = [current_patch_scaling, 0, 0, 0, 0, 0]';
% the reference shape
refShape = GetShapeOrtho(M, PDM.V, local_params, refGlobal);
% shape around which the patch experts will be evaluated in the original image
[shape2D] = GetShapeOrtho(M, PDM.V, local_params, global_params);
shape2D_img = shape2D(:,1:2);
% Create transform using a slightly modified version of Kabsch that
% takes scaling into account as well, in essence we get a
% similarity transform from current estimate to reference shape
[A_img2ref, T_img2ref, ~, ~] = AlignShapesWithScale(shape2D_img(:,1:2),refShape(:,1:2));
% Create a transform, from shape in image to reference shape
T = maketform('affine', [A_img2ref;T_img2ref]);
shape_2D_ref = tformfwd(T, shape2D_img);
% transform the current shape to the reference one, so we can
% interpolate
shape2D_in_ref = (A_img2ref * shape2D_img')';
sideSizeX = (clmParams.window_size(i,1) - 1)/2;
sideSizeY = (clmParams.window_size(i,2) - 1)/2;
patches = zeros(size(shape2D_in_ref,1), clmParams.window_size(i,1) * clmParams.window_size(i,2));
Ainv = inv(A_img2ref);
% extract patches on which patch experts will be evaluted
for l=1:size(shape2D_in_ref,1)
if(visibilities(view,l))
xs = (shape2D_in_ref(l,1)-sideSizeX):(shape2D_in_ref(l,1)+sideSizeX);
ys = (shape2D_in_ref(l,2)-sideSizeY):(shape2D_in_ref(l,2)+sideSizeY);
[xs, ys] = meshgrid(xs, ys);
pairs = [xs(:), ys(:)];
actualLocs = (Ainv * pairs')';
actualLocs(actualLocs(:,1) < 0,1) = 0;
actualLocs(actualLocs(:,2) < 0,2) = 0;
actualLocs(actualLocs(:,1) > widthImg - 1,1) = widthImg - 1;
actualLocs(actualLocs(:,2) > heightImg - 1,2) = heightImg - 1;
[t_patch] = interp2_mine(xi, yi, GrayImageDb, actualLocs(:,1), actualLocs(:,2), 'bilinear');
t_patch = reshape(t_patch, size(xs));
patches(l,:) = t_patch(:);
end
end
% Calculate patch responses, either SVR or CCNF
if(strcmp(patchExperts(scale).type, 'SVR'))
responses = PatchResponseSVM_multi_modal( patches, patchExperts(scale).patch_experts(view,:), visibilities(view,:), patchExperts(scale).normalisationOptionsCol, clmParams, clmParams.window_size(i,:));
for r=1:numel(responses)
out_patch = reshape(patches(r,:)/255, size(xs));
imwrite(out_patch, [out_dir, '/', num2str(r), '_a.png']);
imwrite(responses{r}/max(responses{r}(:)), [out_dir, '/', num2str(r), '_svr.png']);
end
elseif(strcmp(patchExperts(scale).type, 'CCNF'))
responses = PatchResponseCCNF( patches, patchExperts(scale).patch_experts(view,:), visibilities(view,:), patchExperts(scale), clmParams.window_size(i,:));
for r=1:numel(responses)
imwrite(responses{r}/max(responses{r}(:)), [out_dir, '/', num2str(r), '_lnf.png']);
end
elseif(strcmp(patchExperts(scale).type, 'DNN'))
responses = PatchResponseDNN( patches, patchExperts(scale).patch_experts(view,:), visibilities(view,:), patchExperts(scale), clmParams.window_size(i,:));
for r=1:numel(responses)
imwrite(responses{r}/max(responses{r}(:)), [out_dir, '/', num2str(r), '_dnn.png']);
end
end
end
function [id] = GetView(centers, rotation)
[~,id] = min(sum((centers * pi/180 - repmat(rotation', size(centers,1), 1)).^2,2));
end