diff --git a/.gitignore b/.gitignore
index 10137734..734c9222 100644
--- a/.gitignore
+++ b/.gitignore
@@ -53,3 +53,20 @@ matlab_version/experiments_JANUS/all_fit_cvpr/
 matlab_version/experiments_JANUS/cfss_fit/
 matlab_runners/Feature Point Experiments/out_dclm/
 matlab_runners/Feature Point Experiments/300VW_features_dclm/
+exe/FaceLandmarkVid/Debug/
+lib/3rdParty/dlib/Debug/
+lib/local/FaceAnalyser/Debug/
+lib/local/LandmarkDetector/Debug/
+matlab_version/experiments_menpo/menpo_fit_clnf/
+matlab_version/experiments_menpo/menpo_fit_dclm/
+matlab_version/experiments_menpo/menpo_fit_dclm_bbox_corr/
+matlab_version/experiments_menpo/menpo_fit_dclm_more_hyp/
+matlab_version/experiments_menpo/menpo_fit_ceclm/
+matlab_version/experiments_menpo/menpo_fit_ceclm_test_profile/
+matlab_version/experiments_menpo/menpo_fit_ceclm_test_semifrontal/
+matlab_version/experiments_menpo/menpo_fit_dclm_pdm_cen/
+matlab_version/experiments_menpo/menpo_fit_dclm_pdm_cen_2/
+matlab_version/experiments_menpo/out_profile/
+matlab_version/experiments_menpo/out_semifrontal/
+matlab_version/pdm_generation/menpo_pdm/old_models/
+matlab_version/pdm_generation/menpo_pdm/
diff --git a/matlab_version/models/dpn/create_dpn_experts_2017_02_02.m b/matlab_version/models/dpn/create_dpn_experts_2017_02_02.m
index 75bab417..bbd528df 100644
--- a/matlab_version/models/dpn/create_dpn_experts_2017_02_02.m
+++ b/matlab_version/models/dpn/create_dpn_experts_2017_02_02.m
@@ -50,15 +50,14 @@ for s=scales
                            rel_id = mirrorInds(mirrorInds(:,1)==i,2);
                        end
                        if(~visiIndex(c, rel_id))
-                           break;
+                           continue;
                        end
                        if(c == frontalView)
                            rel_file = sprintf('D:/deep_experts/2017-02-02/rmses/MultiGeneral_arch4general_%s_frontal_%d_512.mat', s{1}, rel_id);
-                       else
-                           rel_file = sprintf('D:/deep_experts/2017-02-02/rmses/MultiGeneral_arch4general_%s_profile%d_%d_512.mat', s{1}, c-1, rel_id);                    
+
+                           mirror = true;
+                           load(rel_file);
                        end
-                       mirror = true;
-                       load(rel_file);
                     end
                     patch_experts.correlations(c, i) = correlation_2;
                     patch_experts.rms_errors(c, i) = rmse;
@@ -107,6 +106,6 @@ for s=scales
         end
     end
     trainingScale = str2num(s{1});
-    save(['dpn_patches_', s{1} '_general2.mat'], 'trainingScale', 'centers', 'visiIndex', 'patch_experts', 'normalisationOptions');
-    write_patch_expert_bin(['dpn_patches_', s{1} '_general2.dat'], trainingScale, centers, visiIndex, patch_experts);
+    save(['dpn_patches_', s{1} '_general_v2.mat'], 'trainingScale', 'centers', 'visiIndex', 'patch_experts', 'normalisationOptions');
+%     write_patch_expert_bin(['dpn_patches_', s{1} '_general_v2.dat'], trainingScale, centers, visiIndex, patch_experts);
 end
\ No newline at end of file
diff --git a/matlab_version/models/pdm/pdm_68_aligned_menpo.mat b/matlab_version/models/pdm/pdm_68_aligned_menpo.mat
new file mode 100644
index 00000000..3d40bba3
Binary files /dev/null and b/matlab_version/models/pdm/pdm_68_aligned_menpo.mat differ
diff --git a/matlab_version/pdm_generation/PDM_helpers/Rot2Euler.m b/matlab_version/pdm_generation/PDM_helpers/Rot2Euler.m
index 470bcf99..2721212c 100644
--- a/matlab_version/pdm_generation/PDM_helpers/Rot2Euler.m
+++ b/matlab_version/pdm_generation/PDM_helpers/Rot2Euler.m
@@ -1,12 +1,16 @@
 function [euler] = Rot2Euler(R)
 
     q0 = sqrt( 1 + R(1,1) + R(2,2) + R(3,3) ) / 2;
-    q1 = (R(3,2) - R(2,3)) / (4*q0) ;
-    q2 = (R(1,3) - R(3,1)) / (4*q0) ;
-    q3 = (R(2,1) - R(1,2)) / (4*q0) ;
+    if(q0 ~= 0)
+        q1 = (R(3,2) - R(2,3)) / (4*q0) ;
+        q2 = (R(1,3) - R(3,1)) / (4*q0) ;
+        q3 = (R(2,1) - R(1,2)) / (4*q0) ;
 
-    yaw  = asin(2*(q0*q2 + q1*q3));
-    pitch= atan2(2*(q0*q1-q2*q3), q0*q0-q1*q1-q2*q2+q3*q3); 
-    roll = atan2(2*(q0*q3-q1*q2), q0*q0+q1*q1-q2*q2-q3*q3);
-    
-    euler = [pitch, yaw, roll];
+        yaw  = asin(2*(q0*q2 + q1*q3));
+        pitch= atan2(2*(q0*q1-q2*q3), q0*q0-q1*q1-q2*q2+q3*q3); 
+        roll = atan2(2*(q0*q3-q1*q2), q0*q0+q1*q1-q2*q2-q3*q3);
+
+        euler = [pitch, yaw, roll];
+    else
+        euler = [0, 0, 0];
+    end
\ No newline at end of file
diff --git a/matlab_version/pdm_generation/PDM_helpers/fit_PDM_ortho_proj_to_2D.m b/matlab_version/pdm_generation/PDM_helpers/fit_PDM_ortho_proj_to_2D.m
index c331ec16..803158ea 100644
--- a/matlab_version/pdm_generation/PDM_helpers/fit_PDM_ortho_proj_to_2D.m
+++ b/matlab_version/pdm_generation/PDM_helpers/fit_PDM_ortho_proj_to_2D.m
@@ -319,13 +319,13 @@ function [non_rigid, rigid] = CalcReferenceUpdate(params_delta, current_non_rigi
                -wy, wx, 1];
 	
 	% Make sure R_delta is orthonormal
-	R_delta = OrthonormaliseRotation(R_delta);
+	R_delta = double(OrthonormaliseRotation(R_delta));
 	
     % Combine rotations
 	R_final = R * R_delta;
 
 	% Extract euler angle
-	euler = Rot2Euler(R_final);	
+	euler = real(Rot2Euler(R_final));	
 	
 	rigid(2:4) = euler;
     
diff --git a/matlab_version/pdm_generation/Readme.txt b/matlab_version/pdm_generation/Readme.txt
index d48124fa..1f0669a5 100644
--- a/matlab_version/pdm_generation/Readme.txt
+++ b/matlab_version/pdm_generation/Readme.txt
@@ -1,6 +1,7 @@
 Creating the Point Distribution Model.
 
-To create a model from wild data use the Matlab script:
+---------------------------------------------------------------
+To create a model from 300W training data use the Matlab script:
 
 ./Wild_data_pdm/Create_pdm_wild.m (This might take up to a couple of hours, depending on the machine used, and if you compiled computeH - see readme in nrsfm-em folder)
 
@@ -8,17 +9,42 @@ You need the training data which can be acquired from (http://ibug.doc.ic.ac.uk/
 
 The script will produce "./Wild_data_pdm/pdm_68_aligned_wild.mat" and "./Wild_data_pdm/pdm_68_aligned_wild.txt" which can be used for landmark detection.
 
+---------------------------------------------------------------
+To create a model from Menpo + 300W training data use the Matlab script:
+
+First convert menpo to train/validation folds (PDM training is done on training fold):
+'./menpo_pdm/split_menpo_data.m'
+
+Followed by:
+Collect_menpo_annotations;
+Collect_menpo_annotations_valid;
+
+To train the actual PDM:
+'./menpo_pdm/Create_pdm_wild.m' (This might take up to a couple of hours, depending on the machine used, and if you compiled computeH - see readme in nrsfm-em folder)
+
+You need the training data which can be acquired from (https://ibug.doc.ic.ac.uk/resources/300-W/, and https://ibug.doc.ic.ac.uk/resources/2nd-facial-landmark-tracking-competition-menpo-ben/), to run the script from scratch. Alternatively the data is collected in 'menpo_68_pts.mat', so you can skip the Collect_wild_annotations step.
+
+The script will produce "./menpo_pdm/pdm_68_aligned_menpo.mat" and "./menpo_pdm/pdm_68_aligned_menpo.txt" which can be used for landmark detection.
+
+-------
+As Menpo challenge expects profile faces to have chin outline, so to learn the conversion to Menpo format use "menpo_chin/learn_menpo_profile_mapping.m" (to get data you will also need to run Collect_menpo_annotations_profile_train.m, Collect_menpo_annotations_profile_valid.m)
+
+---------------------------------------------------------------
 To visualise the results use:
 visualise_PDMs.m
 
 The PDM triangulation was created using the Delaunay triangulation algorithm, with manual hole cutting for eyes and mouth. Same can be done on any other annotated face dataset.
 
-The pdm used in "in-the-wild" experiments is already included as:
+The pdm used in "in-the-wild" and "menpo" experiments is already included as:
 ./Wild_data_pdm/pdm_68_aligned_wild
 ./Wild_data_pdm/pdm_68_aligned_wild.txt
 
+./menpo_pdm/pdm_68_aligned_menpo.mat
+./menpo_pdm/pdm_68_aligned_menpo.txt
+
 The same model should be generated using the Matlab script as well (overwriting the data).
 
+--------------------------------------------------------------
 We use the non-rigid structure from motion approach by Lorenzo Torresani, Aaron Hertzmann, Chris Bregler, "Learning Non-Rigid 3D Shape from 2D Motion", NIPS 16, 2003
 http://cs.stanford.edu/~ltorresa/projects/learning-nr-shape/
 Please cite their work and ours if you use this code.
\ No newline at end of file
diff --git a/matlab_version/pdm_generation/menpo_pdm/Analyze_orient_distribution_wmp.m b/matlab_version/pdm_generation/menpo_pdm/Analyze_orient_distribution_wmp.m
deleted file mode 100644
index d3c5f126..00000000
--- a/matlab_version/pdm_generation/menpo_pdm/Analyze_orient_distribution_wmp.m
+++ /dev/null
@@ -1,42 +0,0 @@
-clear
-load('menpo_68_pts_valid.mat');
-addpath('../PDM_helpers');
-
-xs = all_pts(1:end/2,:);
-ys = all_pts(end/2+1:end,:);
-num_imgs = size(xs, 1);
-
-rots = zeros(3, num_imgs);
-errs = zeros(1,num_imgs);
-
-% pdmLoc = ['../../models/pdm/pdm_68_aligned_wild.mat'];
-pdmLoc = ['pdm_68_aligned_menpo_v5.mat'];
-
-load(pdmLoc);
-
-pdm = struct;
-pdm.M = double(M);
-pdm.E = double(E);
-pdm.V = double(V);
-errs_poss = [];
-for i=1:num_imgs
-    
-    labels_curr = cat(2, xs(i,:)', ys(i,:)');
-    labels_curr(labels_curr==-1) = 0;
-
-    [ a, R, T, ~, l_params, err, shapeOrtho] = fit_PDM_ortho_proj_to_2D(pdm.M, pdm.E, pdm.V, labels_curr);
-    errs(i) = err/a;
-    rots(:,i) = Rot2Euler(R);
-    
-    if(errs(i) < 0 || errs(i) > 4)
-       fprintf('i - %d, err - %.3f\n', i, errs(i));
-       errs_poss = cat(1, errs_poss, i);
-    end       
-end
-
-% 300W PDM error is 1.5373
-% Menpo PDM leads to 1.2140 error (100 iters, 50% rem, 25 dof, mirror, annealing 60) 
-% Menpo PDM v2 leads to 1.1488 error (25 dof, 20% rem, 200 iters, annealing 60)
-% Menpo PDM v3 leads to 1.1546 error (25 dof, 10% rem, 300 iters, annealing 60)
-% Menpo PDM v4 leads to 1.1263 error (30 dof, no rem, 200 iters, annealing const 100)
-% Menpo PDM v5 leads to 1.1312 (30 dof, no rem, 200 iters, annealing const 100)
\ No newline at end of file
diff --git a/matlab_version/pdm_generation/menpo_pdm/Collect_menpo_annotations.m b/matlab_version/pdm_generation/menpo_pdm/Collect_menpo_annotations.m
index 1e0a65d4..1f2da08b 100644
--- a/matlab_version/pdm_generation/menpo_pdm/Collect_menpo_annotations.m
+++ b/matlab_version/pdm_generation/menpo_pdm/Collect_menpo_annotations.m
@@ -1,10 +1,11 @@
 % collect the training data annotations (from the menpo challenge)
 clear
-train_data_loc = 'C:\Users\tbaltrus\Documents\menpo_data_orig/';
+train_data_loc = 'D:\Datasets\menpo/';
 
 dataset_locs = {[train_data_loc, '/train/'];};
 
-
+% Mapping all the points (profile and semi-frontal) to the same 68 point
+% annotation scheme
 left_to_frontal_map = [17,28; 18,29; 19,30; 20,31;
                        21,34; 22,32; 23,39; 24,38; 25,37; 26,42; 27,41;
                        28,52; 29,51; 30,50; 31,49; 32,60; 33,59; 34,58;
@@ -23,7 +24,7 @@ add_flip = true;
 mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;...
               32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;...
               61,65;62,64;68,66];
-ps = [401;407;650;862;870;890;1274;1298;1347;1962;2058;2390;2996;3012;3410;4534;4767;5053;5911];
+
 for i=1:numel(dataset_locs)
     landmarkLabels = dir([dataset_locs{i} '\*.pts']);
     landmarkImgs = dir([dataset_locs{i} '\*.jpg']);
@@ -33,127 +34,7 @@ for i=1:numel(dataset_locs)
         img = imread([dataset_locs{i}, landmarkImgs(p).name]);
         landmarks = landmarks.data;
         landmark_labels = -ones(68,2); 
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_42138.jpg'))
-            landmarks(23:27,:) = landmarks(27:-1:23,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_65193.jpg'))
-            landmarks(1:12,:) = landmarks(12:-1:1,:);
-            landmarks(13:16,:) = landmarks(16:-1:13,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_65158.jpg'))
-            landmarks(1:12,:) = landmarks(12:-1:1,:);
-            landmarks(13:16,:) = landmarks(16:-1:13,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_65153.jpg'))
-            landmarks(1:12,:) = landmarks(12:-1:1,:);
-            landmarks(13:16,:) = landmarks(16:-1:13,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_65119.jpg'))
-            landmarks(1:12,:) = landmarks(12:-1:1,:);
-            landmarks(13:16,:) = landmarks(16:-1:13,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_64862.jpg'))
-            landmarks(13:16,:) = landmarks(16:-1:13,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_64849.jpg'))
-            landmarks(1:12,:) = landmarks(12:-1:1,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_64833.jpg'))
-            landmarks(1:12,:) = landmarks(12:-1:1,:);
-            landmarks(13:16,:) = landmarks(16:-1:13,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_64806.jpg'))
-            landmarks([18:22],:) = landmarks(22:-1:18,:);
-        end
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_64744.jpg'))
-            landmarks(1:12,:) = landmarks(12:-1:1,:);
-            landmarks(13:16,:) = landmarks(16:-1:13,:);
-        end
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_64292.jpg'))
-            landmarks(13:16,:) = landmarks(16:-1:13,:);
-        end
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_64170.jpg'))                
-            landmarks([1:12],:) = landmarks(12:-1:1,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_64167.jpg'))
-            landmarks([1:12],:) = landmarks(12:-1:1,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_63590.jpg'))
-            landmarks([18:22],:) = landmarks(22:-1:18,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_63190.jpg'))
-            landmarks(1:12,:) = landmarks(12:-1:1,:);
-            landmarks(13:16,:) = landmarks(16:-1:13,:);
-        end
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_63118.jpg'))                
-            landmarks([1:12],:) = landmarks(12:-1:1,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_62990.jpg'))
-            landmarks([18:22],:) = landmarks(22:-1:18,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_62534.jpg'))
-            landmarks([18:22],:) = landmarks(22:-1:18,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_47686.jpg'))                
-            landmarks([64,63,62,61,68,67,66,65],:) = landmarks(61:68,:);
-        end
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_47687.jpg'))                
-            landmarks([64,63,62,61,68,67,66,65],:) = landmarks(61:68,:);
-        end        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_45522.jpg'))
-            outline = landmarks(1:17,:);
-            outline(12:13,:) = [];
-            outline = iterate_piece_wise(outline, 17);
-                
-            landmarks(1:17,:) = outline;
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_43536.jpg'))
-            landmarks(17:22,:) = landmarks(22:-1:17,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_42898.jpg'))
-            landmarks(28:31,:) = landmarks(31:-1:28,:);
-        end
-        
-        % Problem with the labels
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_41716.jpg'))
-            landmarks(23:27,:) = landmarks(27:-1:23,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_41487.jpg'))
-            landmarks(28:34,:) = landmarks(34:-1:28,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_41364.jpg'))
-            landmarks(37:48,:) = landmarks([43:48, 37:42],:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_63080.jpg'))
-            landmarks(1:12,:) = landmarks(12:-1:1,:);
-            landmarks(13:16,:) = landmarks(16:-1:13,:);
-        end
-           
-        % Problem with the labels
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_63001.jpg'))
-            landmarks(18:27,:) = landmarks([23:27, 18:22],:);
-        end
-        
+                        
         if(size(landmarks,1) == 39)
             % Determine if the points are clock-wise or counter clock-wise
             % Clock-wise points are facing left, counter-clock-wise right
@@ -205,8 +86,16 @@ for i=1:numel(dataset_locs)
     
     end
 end
+all_pts_old = all_pts;
+%%
+xs = squeeze(all_pts(:,1,:));
+ys = squeeze(all_pts(:,2,:));
+
+all_pts = cat(2,xs,ys)';
+save('menpo_68_pts', 'all_pts');
 
 %%
+all_pts = all_pts_old ;
 all_pts = cat(3, all_pts, mirr_pts);
 
 xs = squeeze(all_pts(:,1,:));
diff --git a/matlab_version/pdm_generation/menpo_pdm/Collect_menpo_annotations_valid.m b/matlab_version/pdm_generation/menpo_pdm/Collect_menpo_annotations_valid.m
index 858fa7a9..a56e4dda 100644
--- a/matlab_version/pdm_generation/menpo_pdm/Collect_menpo_annotations_valid.m
+++ b/matlab_version/pdm_generation/menpo_pdm/Collect_menpo_annotations_valid.m
@@ -1,6 +1,6 @@
 % collect the training data annotations (from the menpo challenge)
 clear
-train_data_loc = 'C:\Users\tbaltrus\Documents\menpo_data_orig/';
+train_data_loc = 'D:\Datasets\menpo/';
 
 dataset_locs = {[train_data_loc, '/valid/'];};
 
@@ -27,54 +27,7 @@ for i=1:numel(dataset_locs)
         img = imread([dataset_locs{i}, landmarkImgs(p).name]);
         landmarks = landmarks.data;
         landmark_labels = -ones(68,2); 
-
-        % Problem with the labels        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_63514.jpg'))
-            % The annotation style is quite different
-            continue;
-        end
-        
-        % Problem with the labels        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_65249.jpg'))
-            landmarks(1:12,:) = landmarks(12:-1:1,:);
-            landmarks(13:16,:) = landmarks(16:-1:13,:);
-        end
-        
-        % Problem with the labels
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_64866.jpg'))
-            landmarks(1:12,:) = landmarks(12:-1:1,:);
-        end
-        
-        % Problem with the labels
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_64771.jpg'))
-            landmarks(1:12,:) = landmarks(12:-1:1,:);
-            landmarks(13:16,:) = landmarks(16:-1:13,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_64735.jpg'))
-            landmarks(13:16,:) = landmarks(16:-1:13,:);
-        end
-        
-        % Problem with the labels
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_64238.jpg'))
-            landmarks(18:22,:) = landmarks(22:-1:18,:);
-        end
-        
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_43770.jpg'))
-            landmarks(13:16,:) = landmarks(16:-1:13,:);
-        end
                 
-        % Problem with the labels
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_63080.jpg'))
-            landmarks(1:12,:) = landmarks(12:-1:1,:);
-            landmarks(13:16,:) = landmarks(16:-1:13,:);
-        end
-           
-        % Problem with the labels
-        if(strcmp(landmarkImgs(p).name, 'aflw__face_63001.jpg'))
-            landmarks(18:27,:) = landmarks([23:27, 18:22],:);
-        end
-        
         if(size(landmarks,1) == 39)
             % Determine if the points are clock-wise or counter clock-wise
             % Clock-wise points are facing left, counter-clock-wise right
diff --git a/matlab_version/pdm_generation/menpo_pdm/Create_pdm_menpo.m b/matlab_version/pdm_generation/menpo_pdm/Create_pdm_menpo.m
index 2b8cbea2..af175ffe 100644
--- a/matlab_version/pdm_generation/menpo_pdm/Create_pdm_menpo.m
+++ b/matlab_version/pdm_generation/menpo_pdm/Create_pdm_menpo.m
@@ -2,13 +2,11 @@ clear;
 
 addpath('../PDM_helpers/');
 
-% load('menpo_68_pts.mat');
-
-% Reconstruct_Torresani;
+Reconstruct_Torresani;
 
 %% Create the PDM using PCA, from the recovered 3D data
 clear
-load('Torr_wild_200_no_rem_wild_menpo.mat');
+load('Torr_menpo_wild.mat');
 
 % need to still perform procrustes though
 
@@ -94,7 +92,10 @@ end
 V = V_aligned;
 M = M_aligned;
 
-save('pdm_68_aligned_menpo_v5.mat', 'E', 'M', 'V');
-
-writePDM(V, E, M, 'pdm_68_aligned_wild.txt');
+save('pdm_68_aligned_menpo.mat', 'E', 'M', 'V');
+writePDM(V, E, M, 'pdm_68_aligned_menpo.txt');
 
+% also save this to model location
+if(exist('../../models/pdm/', 'file'))
+    save('../../models/pdm/pdm_68_aligned_menpo.mat', 'E', 'M', 'V');
+end
diff --git a/matlab_version/pdm_generation/menpo_pdm/Reconstruct_Torresani.m b/matlab_version/pdm_generation/menpo_pdm/Reconstruct_Torresani.m
index e2b3e839..bd988e3e 100644
--- a/matlab_version/pdm_generation/menpo_pdm/Reconstruct_Torresani.m
+++ b/matlab_version/pdm_generation/menpo_pdm/Reconstruct_Torresani.m
@@ -1,6 +1,19 @@
+% This is the final model used for 3D PDM, it contains both Menpo and 300W
+% data
 clear
 
+% Combining 300W and Menpo data for PDM
 load('menpo_68_pts_flip');
+num_pts = size(all_pts,1)/2;
+xs_m = all_pts(1:num_pts,:);
+ys_m = all_pts(num_pts+1:end,:);
+
+load('wild_68_pts');
+num_pts = size(all_pts,1)/2;
+xs_w= all_pts(1:num_pts,:);
+ys_w = all_pts(num_pts+1:end,:);
+
+all_pts = cat(1, xs_m, xs_w, ys_m, ys_w);
 
 pdmLoc = ['../../models/pdm/pdm_68_aligned_wild.mat'];
 load(pdmLoc);
@@ -64,9 +77,9 @@ ys = all_pts(num_pts+1:end,:);
 
 % Randperm the data, as a test
 
-% xs_f = xs(frontal,:);
-% ys_f = ys(frontal,:);
-% scatter(xs_f(:), -ys_f(:));
+xs_f = xs(frontal,:);
+ys_f = ys(frontal,:);
+scatter(xs_f(:), -ys_f(:));
 
 %% Perform NRSFM by Torresani 
 addpath('../nrsfm-em');
@@ -84,7 +97,7 @@ MD = all_pts(1:end/2,:)==-1;
 
 [P3, S_hat, V, RO, Tr, Z] = em_sfm(all_pts, MD, K, use_lds, tol, max_em_iter);
 
-save('Torr_wild_100_no_rem', 'P3', 'S_hat', 'V', 'RO', 'Tr', 'Z');
+save('Torr_menpo_wild', 'P3', 'S_hat', 'V', 'RO', 'Tr', 'Z');
 
 %%
 % xs = P3(1:num_pts,:);
diff --git a/matlab_version/pdm_generation/menpo_pdm/Reconstruct_Torresani_m_and_w.m b/matlab_version/pdm_generation/menpo_pdm/Reconstruct_Torresani_m_and_w.m
deleted file mode 100644
index 8fb0433d..00000000
--- a/matlab_version/pdm_generation/menpo_pdm/Reconstruct_Torresani_m_and_w.m
+++ /dev/null
@@ -1,110 +0,0 @@
-% This is the final model used for 3D PDM, it contains both Menpo and 300W
-% data
-clear
-
-% Combining 300W and Menpo data for PDM
-load('menpo_68_pts_flip');
-num_pts = size(all_pts,1)/2;
-xs_m = all_pts(1:num_pts,:);
-ys_m = all_pts(num_pts+1:end,:);
-
-load('wild_68_pts');
-num_pts = size(all_pts,1)/2;
-xs_w= all_pts(1:num_pts,:);
-ys_w = all_pts(num_pts+1:end,:);
-
-all_pts = cat(1, xs_m, xs_w, ys_m, ys_w);
-
-pdmLoc = ['../../models/pdm/pdm_68_aligned_wild.mat'];
-load(pdmLoc);
-% Before plugging into NRSFM, align the points in 2D
-num_pts = size(all_pts,1)/2;
-    
-num_lmks = numel(M) / 3;
-m = reshape(M, num_lmks, 3)';
-width_model = max(m(1,:)) - min(m(1,:));
-height_model = max(m(2,:)) - min(m(2,:));
-    
-for i=1:size(all_pts,1)/2
-    
-    shape2D = cat(2, all_pts(i,:)', all_pts(i+num_pts,:)'); 
-    
-    M_n = M;
-
-    if(sum(shape2D(:)==-1) > 0)        
-
-        hidden = true;
-        % which indices to remove
-        inds_to_rem = shape2D(:,1) == -1 | shape2D(:,2) == -1;
-
-        shape2D = shape2D(~inds_to_rem,:);
-
-        inds_to_rem = repmat(inds_to_rem, 3, 1);
-
-        M_n = M(~inds_to_rem);
-        
-    end
-    
-    % To deal with really extreme cases of roll
-    M2D = cat(2, M_n(1:end/3), M_n(end/3+1:2*end/3));
-    [ A, t, error, alignedShape, s ] = AlignShapesWithScale(M2D, shape2D);
-    R = A/s;
-    
-    % Transform the shape
-    shape2D(:,1) = shape2D(:,1) - t(1);
-    shape2D(:,2) = shape2D(:,2) - t(2);
-    
-    shape2D = (R' * shape2D')/s;
-    shape2D = shape2D';
-
-    all_pts(i,all_pts(i,:)~=-1) = shape2D(:,1);
-    all_pts(i+num_pts,all_pts(i+num_pts,:)~=-1) = shape2D(:,2);
-end
-
-%%
-% to_rem = randperm(round(0.1*num_pts)); % Remove 10% to break some symmetry
-% all_pts([to_rem, to_rem+num_pts],:) = [];
-num_pts = size(all_pts,1)/2;
-
-left_ids = all_pts(1:num_pts,10) == -1;
-right_ids = all_pts(1:num_pts,8) == -1;
-frontal = true(num_pts,1);
-frontal(left_ids | right_ids) = false;
-
-%%
-xs = all_pts(1:num_pts,:);
-ys = all_pts(num_pts+1:end,:);
-
-% Randperm the data, as a test
-
-xs_f = xs(frontal,:);
-ys_f = ys(frontal,:);
-scatter(xs_f(:), -ys_f(:));
-
-%% Perform NRSFM by Torresani 
-addpath('../nrsfm-em');
-% (T is the number of frames, J is the number of points)
-
-J = size(all_pts,2);
-T = size(all_pts,1)/2;
-
-use_lds = 0; % not modeling a linear dynamic system here
-max_em_iter = 200;
-tol = 0.001;
-K = 30; % number of deformation shapes
-
-MD = all_pts(1:end/2,:)==-1;
-
-[P3, S_hat, V, RO, Tr, Z] = em_sfm(all_pts, MD, K, use_lds, tol, max_em_iter);
-
-save('Torr_wild_200_no_rem_wild_menpo', 'P3', 'S_hat', 'V', 'RO', 'Tr', 'Z');
-
-%%
-% xs = P3(1:num_pts,:);
-% ys = P3(num_pts+1:2*num_pts,:);
-% zs = P3(2*num_pts+1:end,:);
-% % 
-% xs_f = xs(left_ids,:);
-% ys_f = ys(left_ids,:);
-% zs_f = zs(left_ids,:);
-% scatter3(xs_f(:), ys_f(:), zs_f(:));
\ No newline at end of file
diff --git a/matlab_version/pdm_generation/menpo_pdm/Analyze_orient_distribution.m b/matlab_version/pdm_generation/menpo_pdm/helpers/Analyze_orient_distribution.m
similarity index 53%
rename from matlab_version/pdm_generation/menpo_pdm/Analyze_orient_distribution.m
rename to matlab_version/pdm_generation/menpo_pdm/helpers/Analyze_orient_distribution.m
index 198a569d..50c3f82f 100644
--- a/matlab_version/pdm_generation/menpo_pdm/Analyze_orient_distribution.m
+++ b/matlab_version/pdm_generation/menpo_pdm/helpers/Analyze_orient_distribution.m
@@ -1,6 +1,8 @@
+% A script for visualizing the orientations in the menpo data, allows to
+% see the distributions of the data
 clear
-load('menpo_68_pts.mat');
-addpath('../PDM_helpers');
+load('../menpo_68_pts.mat');
+addpath('../../PDM_helpers');
 
 xs = all_pts(1:end/2,:);
 ys = all_pts(end/2+1:end,:);
@@ -9,8 +11,7 @@ num_imgs = size(xs, 1);
 rots = zeros(3, num_imgs);
 errs = zeros(1,num_imgs);
 
-% pdmLoc = ['../../models/pdm/pdm_68_aligned_wild.mat'];
-pdmLoc = ['pdm_68_aligned_menpo_v3.mat'];
+pdmLoc = ['../pdm_68_aligned_menpo.mat'];
 
 load(pdmLoc);
 
@@ -28,14 +29,6 @@ for i=1:num_imgs
     errs(i) = err/a;
     rots(:,i) = Rot2Euler(R);
     
-    if(errs(i) < 0 || errs(i) > 4)
-       fprintf('i - %d, err - %.3f\n', i, errs(i));
-       errs_poss = cat(1, errs_poss, i);
-    end       
 end
 
-% Current error is 2.1728 on the training data
-% After cleanup and smaller steps it is 1.5388
-% After training on it we have 1.1999
-% V2 - 1.1392
-% V3 - 1.1406
\ No newline at end of file
+hist(rots', 100);
\ No newline at end of file
diff --git a/matlab_version/pdm_generation/menpo_pdm/Collect_wild_imgs_train.m b/matlab_version/pdm_generation/menpo_pdm/helpers/Collect_wild_imgs_train.m
similarity index 100%
rename from matlab_version/pdm_generation/menpo_pdm/Collect_wild_imgs_train.m
rename to matlab_version/pdm_generation/menpo_pdm/helpers/Collect_wild_imgs_train.m
diff --git a/matlab_version/pdm_generation/menpo_pdm/Evaluate_pdm.m b/matlab_version/pdm_generation/menpo_pdm/helpers/Evaluate_pdm.m
similarity index 62%
rename from matlab_version/pdm_generation/menpo_pdm/Evaluate_pdm.m
rename to matlab_version/pdm_generation/menpo_pdm/helpers/Evaluate_pdm.m
index 366cedb0..04ae0fc2 100644
--- a/matlab_version/pdm_generation/menpo_pdm/Evaluate_pdm.m
+++ b/matlab_version/pdm_generation/menpo_pdm/helpers/Evaluate_pdm.m
@@ -1,6 +1,6 @@
 clear
-load('menpo_68_pts_valid.mat');
-addpath('../PDM_helpers');
+load('../menpo_68_pts_valid.mat');
+addpath('../../PDM_helpers');
 
 xs = all_pts(1:end/2,:);
 ys = all_pts(end/2+1:end,:);
@@ -9,8 +9,7 @@ num_imgs = size(xs, 1);
 rots_menpo = zeros(3, num_imgs);
 errs_menpo = zeros(1,num_imgs);
 
-% pdmLoc = ['../../models/pdm/pdm_68_aligned_wild.mat'];
-pdmLoc = ['pdm_68_aligned_menpo_v5.mat'];
+pdmLoc = ['../pdm_68_aligned_menpo.mat'];
 
 load(pdmLoc);
 
@@ -48,16 +47,8 @@ for i=1:num_imgs
     errs_wild(i) = err/a;
     rots_wild(:,i) = Rot2Euler(R);
     
-    if(errs_wild(i) < 0 || errs_wild(i) > 4)
-       fprintf('i - %d, err - %.3f\n', i, errs_wild(i));
-       errs_poss = cat(1, errs_poss, i);
-    end       
 end
 errs = cat(2, errs_wild, errs_menpo);
 fprintf('%.3f, %.3f, %.3f\n', mean(errs_menpo), mean(errs_wild), mean(errs));
 % 300W PDM error is 1.537, 1.017, 1.285
-% Menpo PDM v1 leads to 1.214, 1.184, 1.200 error (100 iters, 50% rem, 25 dof, mirror, annealing 60) 
-% Menpo PDM v2 leads to 1.149, 1.138, 1.144 error (25 dof, 20% rem, 200 iters, annealing 60)
-% Menpo PDM v3 leads to 1.155, 1.137, 1.146 error (25 dof, 10% rem, 300 iters, annealing 60)
-% Menpo PDM v4 leads to 1.126, 1.066, 1.097 error (30 dof, no rem, 200 iters, annealing const 100)
-% Menpo PDM v5 leads to 1.133, 0.986, 1.062 (30 dof, no rem, 200 iters, annealing const 100)
\ No newline at end of file
+% Menpo+300W PDM error is 1.130, 0.985, 1.060
\ No newline at end of file
diff --git a/matlab_version/pdm_generation/menpo_pdm/Prepare_annotations_autoenc.m b/matlab_version/pdm_generation/menpo_pdm/helpers/Prepare_annotations_autoenc.m
similarity index 98%
rename from matlab_version/pdm_generation/menpo_pdm/Prepare_annotations_autoenc.m
rename to matlab_version/pdm_generation/menpo_pdm/helpers/Prepare_annotations_autoenc.m
index 96148296..2bce6a45 100644
--- a/matlab_version/pdm_generation/menpo_pdm/Prepare_annotations_autoenc.m
+++ b/matlab_version/pdm_generation/menpo_pdm/helpers/Prepare_annotations_autoenc.m
@@ -1,3 +1,5 @@
+% If a different type of PDM is to be trained, prepare training data here
+
 clear
 load('menpo_68_pts_flip.mat');
 addpath('../PDM_helpers');
diff --git a/matlab_version/pdm_generation/menpo_pdm/menpo_68_pts.mat b/matlab_version/pdm_generation/menpo_pdm/menpo_68_pts.mat
index 211368a9..af5063c5 100644
Binary files a/matlab_version/pdm_generation/menpo_pdm/menpo_68_pts.mat and b/matlab_version/pdm_generation/menpo_pdm/menpo_68_pts.mat differ
diff --git a/matlab_version/pdm_generation/menpo_pdm/menpo_68_pts_valid.mat b/matlab_version/pdm_generation/menpo_pdm/menpo_68_pts_valid.mat
index fe0a5be3..75faa470 100644
Binary files a/matlab_version/pdm_generation/menpo_pdm/menpo_68_pts_valid.mat and b/matlab_version/pdm_generation/menpo_pdm/menpo_68_pts_valid.mat differ
diff --git a/matlab_version/pdm_generation/menpo_pdm/menpo_chin/Collect_menpo_annotations_profile_train.m b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/Collect_menpo_annotations_profile_train.m
new file mode 100644
index 00000000..57f93f2d
--- /dev/null
+++ b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/Collect_menpo_annotations_profile_train.m
@@ -0,0 +1,77 @@
+% collect the training data annotations (from the menpo challenge)
+clear
+train_data_loc = 'C:\Users\tbaltrus\Documents\menpo_data_orig/';
+
+dataset_locs = {[train_data_loc, '/train/'];};
+
+
+left_to_frontal_map = [17,28; 18,29; 19,30; 20,31;
+                       21,34; 22,32; 23,39; 24,38; 25,37; 26,42; 27,41;
+                       28,52; 29,51; 30,50; 31,49; 32,60; 33,59; 34,58;
+                       35,63; 36,62; 37,61; 38,68; 39,67];
+                   
+right_to_frontal_map = [17,28; 18,29; 19,30; 20,31;
+                       21,34; 22,36; 23,44; 24,45; 25,46; 26,47; 27,48;
+                       28,52; 29,53; 30,54; 31,55; 32,56; 33,57; 34,58;
+                       35,63; 36,64; 37,65; 38,66; 39,67];
+            
+all_pts_left = [];
+all_pts_right = [];
+all_pts_orig_left = [];
+all_pts_orig_right = [];
+
+for i=1:numel(dataset_locs)
+    landmarkLabels = dir([dataset_locs{i} '\*.pts']);
+    landmarkImgs = dir([dataset_locs{i} '\*.jpg']);
+
+    for p=1:numel(landmarkLabels)
+        landmarks = importdata([dataset_locs{i}, landmarkLabels(p).name], ' ', 3);
+        img = imread([dataset_locs{i}, landmarkImgs(p).name]);
+        landmarks = landmarks.data;
+        landmark_labels = -ones(68,2); 
+              
+        if(size(landmarks,1) == 39)
+            % Determine if the points are clock-wise or counter clock-wise
+            % Clock-wise points are facing left, counter-clock-wise right
+            sum = 0;
+            for k=1:11
+                step = (landmarks(k+1,1) - landmarks(k,1)) * (landmarks(k+1,2) + landmarks(k,2));
+                sum = sum + step;
+            end
+            
+            if(sum > 0)
+                % First need to resample the face outline as there are 9
+                % points in the near-frontal and 10 points in profile for
+                % the outline of the face
+                
+                outline = iterate_piece_wise(landmarks(1:10,:), 9);
+                brow = iterate_piece_wise(landmarks(13:16,:), 5);
+                landmark_labels(1:9,:) = outline;
+                landmark_labels(18:22,:) = brow;
+                landmark_labels(left_to_frontal_map(:,2),:) = landmarks(left_to_frontal_map(:,1),:);
+                
+                all_pts_orig_right = cat(3, all_pts_orig_right, landmarks);
+                all_pts_right = cat(3, all_pts_right, landmark_labels);
+            else
+                outline = iterate_piece_wise(landmarks(10:-1:1,:), 9);
+                brow = iterate_piece_wise(landmarks(16:-1:13,:), 5);
+                
+                landmark_labels(9:17,:) = outline;
+                landmark_labels(23:27,:) = brow;
+                
+                landmark_labels(right_to_frontal_map(:,2),:) = landmarks(right_to_frontal_map(:,1),:);   
+                
+                all_pts_orig_left = cat(3, all_pts_orig_left, landmarks);
+                all_pts_left = cat(3, all_pts_left, landmark_labels);
+            end
+        end
+        
+        if(mod(p,50) == 0)
+            fprintf('%d/%d\n', p, numel(landmarkLabels))
+        end
+    end
+end
+
+%%
+
+save('menpo_68_pts_train_profile', 'all_pts_orig_right', 'all_pts_right', 'all_pts_orig_left', 'all_pts_left');
diff --git a/matlab_version/pdm_generation/menpo_pdm/menpo_chin/Collect_menpo_annotations_profile_valid.m b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/Collect_menpo_annotations_profile_valid.m
new file mode 100644
index 00000000..fb6013d2
--- /dev/null
+++ b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/Collect_menpo_annotations_profile_valid.m
@@ -0,0 +1,77 @@
+% collect the training data annotations (from the menpo challenge)
+clear
+train_data_loc = 'C:\Users\tbaltrus\Documents\menpo_data_orig/';
+
+dataset_locs = {[train_data_loc, '/valid/'];};
+
+
+left_to_frontal_map = [17,28; 18,29; 19,30; 20,31;
+                       21,34; 22,32; 23,39; 24,38; 25,37; 26,42; 27,41;
+                       28,52; 29,51; 30,50; 31,49; 32,60; 33,59; 34,58;
+                       35,63; 36,62; 37,61; 38,68; 39,67];
+                   
+right_to_frontal_map = [17,28; 18,29; 19,30; 20,31;
+                       21,34; 22,36; 23,44; 24,45; 25,46; 26,47; 27,48;
+                       28,52; 29,53; 30,54; 31,55; 32,56; 33,57; 34,58;
+                       35,63; 36,64; 37,65; 38,66; 39,67];
+            
+all_pts_left = [];
+all_pts_right = [];
+all_pts_orig_left = [];
+all_pts_orig_right = [];
+
+for i=1:numel(dataset_locs)
+    landmarkLabels = dir([dataset_locs{i} '\*.pts']);
+    landmarkImgs = dir([dataset_locs{i} '\*.jpg']);
+
+    for p=1:numel(landmarkLabels)
+        landmarks = importdata([dataset_locs{i}, landmarkLabels(p).name], ' ', 3);
+        img = imread([dataset_locs{i}, landmarkImgs(p).name]);
+        landmarks = landmarks.data;
+        landmark_labels = -ones(68,2); 
+              
+        if(size(landmarks,1) == 39)
+            % Determine if the points are clock-wise or counter clock-wise
+            % Clock-wise points are facing left, counter-clock-wise right
+            sum = 0;
+            for k=1:11
+                step = (landmarks(k+1,1) - landmarks(k,1)) * (landmarks(k+1,2) + landmarks(k,2));
+                sum = sum + step;
+            end
+            
+            if(sum > 0)
+                % First need to resample the face outline as there are 9
+                % points in the near-frontal and 10 points in profile for
+                % the outline of the face
+                
+                outline = iterate_piece_wise(landmarks(1:10,:), 9);
+                brow = iterate_piece_wise(landmarks(13:16,:), 5);
+                landmark_labels(1:9,:) = outline;
+                landmark_labels(18:22,:) = brow;
+                landmark_labels(left_to_frontal_map(:,2),:) = landmarks(left_to_frontal_map(:,1),:);
+                
+                all_pts_orig_right = cat(3, all_pts_orig_right, landmarks);
+                all_pts_right = cat(3, all_pts_right, landmark_labels);
+            else
+                outline = iterate_piece_wise(landmarks(10:-1:1,:), 9);
+                brow = iterate_piece_wise(landmarks(16:-1:13,:), 5);
+                
+                landmark_labels(9:17,:) = outline;
+                landmark_labels(23:27,:) = brow;
+                
+                landmark_labels(right_to_frontal_map(:,2),:) = landmarks(right_to_frontal_map(:,1),:);   
+                
+                all_pts_orig_left = cat(3, all_pts_orig_left, landmarks);
+                all_pts_left = cat(3, all_pts_left, landmark_labels);
+            end
+        end
+        
+        if(mod(p,50) == 0)
+            fprintf('%d/%d\n', p, numel(landmarkLabels))
+        end
+    end
+end
+
+%%
+
+save('menpo_68_pts_valid_profile', 'all_pts_orig_right', 'all_pts_right', 'all_pts_orig_left', 'all_pts_left');
diff --git a/matlab_version/pdm_generation/menpo_pdm/menpo_chin/compute_error_menpo_prof.m b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/compute_error_menpo_prof.m
new file mode 100644
index 00000000..fa24d4f2
--- /dev/null
+++ b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/compute_error_menpo_prof.m
@@ -0,0 +1,34 @@
+function [ error_per_image, frontal_ids ] = compute_error_menpo_prof( 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 = numel(ground_truth_all);
+error_per_image = zeros(num_of_images,1);
+frontal_ids = true(num_of_images,1);
+
+for i =1:num_of_images
+    detected_points      = detected_points_all{i};
+    ground_truth_points  = ground_truth_all{i};
+    num_of_points = size(ground_truth_points,1);
+
+   normalization = (max(ground_truth_points(:,1))-min(ground_truth_points(:,1)))+...
+       (max(ground_truth_points(:,2))-min(ground_truth_points(:,2)));
+   normalization = normalization / 2;    
+               
+    sum=0;
+    for j=1:num_of_points
+        sum = sum+norm(detected_points(j,:)-ground_truth_points(j,:));
+    end
+    error_per_image(i) = sum/(num_of_points*normalization);
+end
+
+end
diff --git a/matlab_version/pdm_generation/menpo_pdm/menpo_chin/conversion.mat b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/conversion.mat
new file mode 100644
index 00000000..6f293347
Binary files /dev/null and b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/conversion.mat differ
diff --git a/matlab_version/pdm_generation/menpo_pdm/menpo_chin/learn_menpo_profile_mapping.m b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/learn_menpo_profile_mapping.m
new file mode 100644
index 00000000..7ab03f09
--- /dev/null
+++ b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/learn_menpo_profile_mapping.m
@@ -0,0 +1,75 @@
+clear
+load('menpo_68_pts_train_profile');
+
+vis_pts_left = all_pts_left(:,1,1) ~= -1;
+xs = all_pts_left(vis_pts_left,:,:);
+xs = squeeze(cat(1, xs(:,1,:), xs(:,2,:)));
+
+ys =  squeeze(cat(1, all_pts_orig_left(:,1,:), all_pts_orig_left(:,2,:)));
+
+a_left = ys /xs;
+a_left(abs(a_left)<0.001) = 0;
+transformed_left = a_left * xs;
+xs_pred_left = transformed_left(1:end/2,:);
+ys_pred_left = transformed_left(end/2+1:end,:);
+
+vis_pts_right = all_pts_right(:,1,1) ~= -1;
+xs = all_pts_right(vis_pts_right,:,:);
+xs = squeeze(cat(1, xs(:,1,:), xs(:,2,:)));
+
+ys =  squeeze(cat(1, all_pts_orig_right(:,1,:), all_pts_orig_right(:,2,:)));
+
+a_right = ys /xs;
+a_right(abs(a_right)<0.001) = 0;
+transformed_right = a_right * xs;
+xs_pred_right = transformed_right(1:end/2,:);
+ys_pred_right = transformed_right(end/2+1:end,:);
+% pred_left = cat(2,transformed_left
+
+%%
+num = 110;
+plot(all_pts_orig_right(:,1,num), -all_pts_orig_right(:,2,num), '.r');
+hold on;
+plot(xs_pred_right(:,num), -ys_pred_right(:,num), '.b');
+hold off;
+
+%% Evaluate on the validation data (see how much error is added)
+
+load('menpo_68_pts_valid_profile');
+
+xs = all_pts_left(vis_pts_left,:,:);
+xs = squeeze(cat(1, xs(:,1,:), xs(:,2,:)));
+
+transformed_left = a_left * xs;
+xs_pred_left = transformed_left(1:end/2,:);
+ys_pred_left = transformed_left(end/2+1:end,:);
+
+shapes = cell(size(ys_pred_left,1),1);
+labels = cell(size(ys_pred_left,1),1);
+
+for i=1:numel(shapes)
+    shapes{i} = cat(2, xs_pred_left(:,i), ys_pred_left(:,i));
+    labels{i} = all_pts_orig_left(:,:,i);
+end
+
+errors_left = compute_error_menpo_prof(labels, shapes);
+
+
+xs = all_pts_right(vis_pts_right,:,:);
+xs = squeeze(cat(1, xs(:,1,:), xs(:,2,:)));
+
+transformed_right = a_right * xs;
+xs_pred_right = transformed_right(1:end/2,:);
+ys_pred_right = transformed_right(end/2+1:end,:);
+
+shapes = cell(size(ys_pred_right,1),1);
+labels = cell(size(ys_pred_right,1),1);
+
+for i=1:numel(shapes)
+    shapes{i} = cat(2, xs_pred_right(:,i), ys_pred_right(:,i));
+    labels{i} = all_pts_orig_right(:,:,i);
+end
+
+errors_right = compute_error_menpo_prof(labels, shapes);
+save('conversion', 'a_left', 'a_right', 'vis_pts_left', 'vis_pts_right');
+% Try with scaling
diff --git a/matlab_version/pdm_generation/menpo_pdm/menpo_chin/menpo_68_pts_train_profile.mat b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/menpo_68_pts_train_profile.mat
new file mode 100644
index 00000000..da27454e
Binary files /dev/null and b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/menpo_68_pts_train_profile.mat differ
diff --git a/matlab_version/pdm_generation/menpo_pdm/menpo_chin/menpo_68_pts_valid_profile.mat b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/menpo_68_pts_valid_profile.mat
new file mode 100644
index 00000000..ce495747
Binary files /dev/null and b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/menpo_68_pts_valid_profile.mat differ
diff --git a/matlab_version/pdm_generation/menpo_pdm/menpo_chin/readme.txt b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/readme.txt
new file mode 100644
index 00000000..3f8e30de
--- /dev/null
+++ b/matlab_version/pdm_generation/menpo_pdm/menpo_chin/readme.txt
@@ -0,0 +1 @@
+This includes scripts necessary to learn the mapping from the detected points using our PDM to the points expected in menpo (the different face outline and chin points in profile).
\ No newline at end of file
diff --git a/matlab_version/pdm_generation/menpo_pdm/pdm_68_aligned_menpo_v5.mat b/matlab_version/pdm_generation/menpo_pdm/pdm_68_aligned_menpo_v5.mat
deleted file mode 100644
index bab5e601..00000000
Binary files a/matlab_version/pdm_generation/menpo_pdm/pdm_68_aligned_menpo_v5.mat and /dev/null differ
diff --git a/matlab_version/pdm_generation/menpo_pdm/pdm_68_aligned_wild.txt b/matlab_version/pdm_generation/menpo_pdm/pdm_68_aligned_wild.txt
deleted file mode 100644
index dcf28a55..00000000
--- a/matlab_version/pdm_generation/menpo_pdm/pdm_68_aligned_wild.txt
+++ /dev/null
@@ -1,421 +0,0 @@
-# The mean values of the components (in mm)
-204
-1
-6
--72.957569 
--72.790558 
--70.871761 
--66.831804 
--59.448919 
--48.080739 
--33.832453 
--17.838100 
--0.010106 
-17.387457 
-32.876924 
-46.722555 
-57.850941 
-65.203250 
-69.301098 
-71.232056 
-71.431519 
--57.560424 
--48.976494 
--37.799113 
--26.110444 
--15.352715 
-15.354187 
-26.154473 
-37.691865 
-48.736613 
-57.124426 
-0.348903 
-0.435330 
-0.533328 
-0.639297 
--14.936522 
--7.611648 
-0.435787 
-8.445935 
-15.497456 
--45.289862 
--36.981135 
--27.916315 
--19.874480 
--28.304740 
--37.236112 
-19.855229 
-27.969805 
-36.928263 
-45.033371 
-37.289251 
-28.498230 
--27.475945 
--17.157145 
--6.452818 
-0.447889 
-7.498765 
-17.971501 
-27.895450 
-18.688252 
-8.710833 
-0.373495 
--7.865124 
--18.034598 
--23.970633 
--6.953296 
-0.426526 
-7.904330 
-24.471046 
-8.124865 
-0.345312 
--7.314243 
--30.889604 
--12.665900 
-5.926763 
-24.187268 
-40.563703 
-54.206297 
-64.996338 
-72.851454 
-75.255674 
-72.622259 
-64.613328 
-53.790915 
-40.161067 
-23.763312 
-5.490077 
--13.067600 
--31.275711 
--51.350644 
--58.056846 
--60.520739 
--59.487462 
--55.958185 
--56.092704 
--59.766953 
--60.889299 
--58.461411 
--51.840887 
--39.223014 
--28.700588 
--18.593816 
--8.160504 
-3.919133 
-5.800681 
-6.649958 
-5.733313 
-3.854472 
--36.643045 
--41.214510 
--41.332710 
--35.628204 
--33.719887 
--33.829829 
--35.846858 
--41.651966 
--41.540380 
--37.013378 
--34.196447 
--33.981514 
-27.451758 
-22.337733 
-19.143868 
-20.762136 
-19.110756 
-22.296212 
-27.247954 
-35.232280 
-39.782700 
-40.779943 
-39.884184 
-35.403198 
-27.722249 
-25.805493 
-26.561872 
-25.749741 
-27.553858 
-31.126043 
-32.053655 
-31.208948 
-54.183375 
-50.965902 
-47.818113 
-44.116655 
-37.698773 
-27.933197 
-15.198874 
-1.501238 
--7.011294 
-0.687352 
-13.495502 
-26.000721 
-35.976559 
-42.922230 
-47.156260 
-50.812657 
-54.616630 
-11.534080 
-4.983904 
--1.434412 
--7.091812 
--11.694770 
--11.874007 
--7.795857 
--2.480679 
-3.755996 
-10.099855 
--13.774893 
--21.256864 
--28.971320 
--36.653755 
--18.293956 
--23.177443 
--26.247199 
--23.312260 
--18.546073 
-7.254469 
-2.213472 
--0.235708 
-0.587214 
--0.441350 
-2.009948 
--0.000295 
--0.977536 
-1.338488 
-6.428877 
-1.152072 
--1.117445 
--7.174301 
--16.106530 
--22.347647 
--23.561368 
--22.416222 
--16.128115 
--7.298468 
--14.363859 
--19.787955 
--21.164948 
--19.525090 
--14.137779 
--8.914601 
--19.710792 
--21.252549 
--19.750011 
--9.023398 
--18.668917 
--20.129951 
--18.590983 
-# The principal components (eigenvectors) of identity or combined identity and expression model
-204
-30
-6
-0.131000 -0.130552 0.089423 -0.053781 0.004657 -0.054775 -0.012357 -0.025496 0.082858 -0.126211 0.091977 0.005194 0.041269 -0.081311 -0.060633 0.004523 -0.052243 0.096304 0.077820 -0.083946 -0.098513 0.182733 -0.155459 0.017432 0.081802 0.008507 -0.081162 -0.008473 -0.025458 -0.019720 
-0.115364 -0.126433 0.094817 -0.027277 -0.020686 -0.051915 -0.017736 -0.008234 0.054350 -0.132171 0.049937 -0.009887 0.049972 -0.053017 -0.034696 0.057242 -0.010454 0.055370 0.061877 -0.034280 -0.078294 0.057551 -0.118920 0.044175 0.080022 -0.001560 -0.062444 -0.001926 0.097419 -0.046021 
-0.100034 -0.128392 0.109316 0.009746 -0.042428 -0.047152 -0.019368 0.008715 0.026194 -0.121356 0.009365 -0.009096 0.044589 -0.022735 0.014639 0.091590 0.045279 0.017624 0.044269 0.045814 -0.056627 -0.065402 -0.064766 0.070601 0.056775 0.010314 -0.045997 -0.001886 0.186356 -0.068462 
-0.086793 -0.129564 0.114761 0.051813 -0.046701 -0.017164 -0.009835 0.011946 0.008575 -0.087736 -0.027875 0.017706 0.015608 0.000864 0.082867 0.097761 0.098016 -0.005552 0.027238 0.113549 0.012621 -0.135885 0.035692 0.055584 0.002885 0.035535 -0.013320 -0.004765 0.156147 -0.052844 
-0.059721 -0.116027 0.094173 0.104355 -0.039262 0.032101 0.006363 0.023790 -0.002056 -0.005908 -0.038421 0.050006 -0.024162 0.011646 0.140333 0.079667 0.118686 -0.017389 0.015720 0.147384 0.103153 -0.113630 0.115588 0.022112 -0.055636 0.028606 0.002730 -0.017969 -0.013690 0.014229 
-0.027944 -0.085450 0.058306 0.136968 -0.036214 0.065977 0.013986 0.040896 -0.010685 0.102869 0.003141 0.067018 -0.061664 0.022892 0.150815 0.035808 0.108932 -0.038607 0.009000 0.151103 0.139141 -0.036196 0.140794 -0.011306 -0.091725 -0.013719 0.028225 -0.029264 -0.167119 0.046116 
-0.004996 -0.040088 0.026824 0.121130 -0.033564 0.067139 0.019032 0.062829 -0.005547 0.193545 0.071544 0.069094 -0.081254 0.055273 0.106692 -0.030068 0.068158 -0.072580 0.009322 0.126033 0.102785 0.036717 0.090226 -0.035507 -0.079112 -0.057653 0.050979 0.014436 -0.201196 0.024758 
--0.004486 -0.007966 0.009112 0.063763 -0.030671 0.030437 -0.005595 0.066568 0.027348 0.244705 0.173817 0.080566 -0.070269 0.089164 0.006247 -0.117501 0.038415 -0.085082 0.044192 0.076545 0.045171 0.068874 0.024907 -0.044591 -0.056441 -0.059591 0.044388 0.100246 -0.061007 -0.056956 
-0.010148 -0.006714 0.001314 -0.003406 -0.024425 -0.025639 -0.048345 0.035074 0.062468 0.230966 0.252671 0.097712 -0.026944 0.100550 -0.087439 -0.136193 0.033910 -0.074112 0.069669 0.023126 0.027556 0.001363 -0.008006 -0.055136 -0.053023 -0.030220 0.018383 0.110400 0.083293 -0.103159 
-0.003647 0.000224 -0.010085 -0.069230 -0.019370 -0.073463 -0.083502 -0.012165 0.090508 0.127544 0.224084 0.073648 0.023379 0.075750 -0.133042 -0.051614 0.032855 -0.068137 0.024881 0.008799 0.006448 -0.114657 -0.004451 -0.034518 0.003911 0.009283 -0.026383 0.070362 0.056194 -0.002159 
-0.027106 0.035431 -0.031037 -0.119530 0.001070 -0.093986 -0.080690 -0.021907 0.105573 0.031113 0.151971 0.019279 0.035706 0.027726 -0.149488 0.037775 0.024234 -0.099875 -0.043297 0.054224 -0.061722 -0.164228 0.009605 -0.002670 0.107323 0.044538 -0.029139 0.050187 -0.024412 0.099770 
-0.057987 0.077353 -0.060974 -0.125712 0.019978 -0.072790 -0.067080 -0.011513 0.108119 -0.004287 0.068853 -0.037940 0.019857 -0.001346 -0.141945 0.065850 -0.000700 -0.130889 -0.087888 0.078396 -0.111414 -0.119664 0.012973 -0.019905 0.174764 0.082617 0.022343 0.010916 -0.061510 0.087908 
-0.091960 0.099667 -0.093230 -0.087333 0.040720 -0.023370 -0.045298 -0.011108 0.076036 -0.010780 -0.015859 -0.064913 -0.007321 -0.027982 -0.105441 0.057128 -0.016374 -0.127413 -0.090495 0.064123 -0.083063 -0.017687 0.002362 -0.055105 0.164410 0.110353 0.076661 -0.043398 -0.073078 -0.014882 
-0.115122 0.105511 -0.110277 -0.031381 0.060428 0.032891 -0.012582 -0.015828 0.026891 0.018579 -0.072101 -0.055134 -0.036099 -0.043018 -0.045001 0.033931 -0.028658 -0.083380 -0.044621 0.003766 0.001487 0.092818 -0.032010 -0.074212 0.092356 0.132619 0.098303 -0.068394 -0.045108 -0.113215 
-0.123086 0.101173 -0.103024 0.013147 0.063638 0.065384 0.007137 -0.019362 -0.011225 0.057962 -0.085434 -0.031784 -0.046444 -0.040408 0.017556 0.016136 -0.041152 -0.005544 0.020995 -0.077645 0.054744 0.132591 -0.086760 -0.046483 -0.009690 0.086281 0.072697 -0.055625 -0.008422 -0.129465 
-0.132799 0.097653 -0.085408 0.050833 0.049749 0.074328 0.008299 0.001513 -0.039437 0.095856 -0.077976 -0.018602 -0.032812 -0.023734 0.054908 -0.001652 -0.052673 0.060442 0.047895 -0.130532 0.056178 0.086905 -0.124387 0.015626 -0.092170 0.001976 0.025809 -0.033228 0.049757 -0.072699 
-0.144926 0.098093 -0.075406 0.079346 0.029923 0.077582 0.001420 0.028720 -0.060867 0.135325 -0.057029 -0.017750 -0.009716 -0.008598 0.065269 -0.011931 -0.062278 0.105316 0.063903 -0.166859 0.060008 0.015669 -0.136936 0.070752 -0.146831 -0.088800 -0.007746 -0.029924 0.117461 0.014301 
-0.020962 0.004442 0.020102 -0.131928 0.222046 -0.004865 0.057660 -0.092237 0.170520 -0.013927 -0.004065 0.069215 0.018130 0.043252 0.006342 -0.018743 -0.049510 -0.017166 -0.194541 -0.079718 0.135770 0.189148 0.050285 0.006011 -0.055052 -0.114091 -0.011054 0.017055 -0.017235 0.168622 
--0.003321 -0.002101 0.024981 -0.092666 0.184187 -0.016551 0.004409 -0.153042 0.150889 0.072888 -0.055672 0.093688 -0.025259 -0.006160 0.026106 -0.045958 -0.040799 0.035704 -0.219942 -0.030219 0.108597 0.054213 -0.000328 0.028840 -0.041588 -0.102863 0.020591 -0.044217 0.017585 0.082863 
--0.023198 -0.025560 0.038565 -0.061130 0.101813 -0.057220 -0.087484 -0.161381 0.095316 0.153777 -0.102682 0.009330 -0.051278 -0.048824 0.010514 -0.073260 -0.027954 0.068664 -0.154554 0.006496 0.098289 -0.037266 -0.023442 0.032017 0.051528 -0.046961 0.033231 -0.026782 -0.022985 0.017342 
--0.044689 -0.044519 0.049370 -0.045554 0.015640 -0.095247 -0.165322 -0.132779 0.035695 0.200910 -0.140974 -0.107909 -0.057514 -0.050269 -0.015489 -0.093860 -0.012175 0.086411 -0.057241 0.016583 0.122035 -0.067475 -0.026356 0.038470 0.163100 0.008375 0.052190 0.008480 -0.079922 -0.046113 
--0.065390 -0.053553 0.055674 -0.030049 -0.059897 -0.125592 -0.216561 -0.082414 -0.020384 0.216137 -0.166720 -0.196444 -0.048967 -0.018694 -0.035643 -0.096529 0.013375 0.099751 0.024071 0.014926 0.155957 -0.062985 -0.020022 0.045828 0.250010 0.044531 0.075596 0.023565 -0.118653 -0.115071 
--0.054943 0.052981 -0.058816 0.051071 0.031448 0.127045 0.132467 0.071848 0.179573 -0.166609 0.133795 0.175469 0.079125 0.100614 0.148934 -0.047969 0.064393 0.018175 0.017209 0.051965 0.071133 -0.105350 -0.097843 -0.010563 0.225571 -0.005753 -0.065043 -0.072606 -0.073144 -0.202777 
--0.029784 0.038087 -0.047453 0.063057 -0.039836 0.103266 0.075313 0.140013 0.168726 -0.127792 0.113768 0.088884 0.078262 0.036680 0.136933 -0.046209 0.052647 0.033044 -0.039913 0.062313 0.100935 -0.075692 -0.063857 -0.014831 0.170606 -0.008788 -0.040396 -0.040159 -0.040067 -0.106729 
--0.005852 0.018304 -0.034915 0.074542 -0.114508 0.076484 0.003493 0.182764 0.144790 -0.084920 0.070502 -0.014054 0.058398 -0.041456 0.107886 -0.057372 0.019830 0.036627 -0.088616 0.057087 0.103430 -0.061968 -0.039866 -0.042605 0.080241 0.004104 -0.021389 0.049864 -0.033601 0.033739 
-0.015044 -0.002120 -0.022053 0.103793 -0.181190 0.050317 -0.074052 0.187131 0.100863 -0.034909 -0.006602 -0.090213 0.024845 -0.114741 0.056543 -0.095262 -0.016606 0.044321 -0.112045 0.049919 0.056226 -0.083936 -0.053566 -0.060897 -0.041563 0.003680 -0.025312 0.132356 -0.027269 0.155327 
-0.043148 -0.007039 -0.019885 0.143232 -0.198663 0.048926 -0.106913 0.132608 0.061425 0.001893 -0.088250 -0.058853 -0.024431 -0.130062 0.012112 -0.126018 -0.034261 0.070034 -0.104998 0.048906 -0.040375 -0.148336 -0.099210 -0.053709 -0.169071 -0.002444 -0.028940 0.097905 0.010401 0.189628 
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--0.089129 -0.029950 0.052159 0.031161 -0.032247 -0.035635 -0.074373 -0.032197 -0.042914 -0.036431 -0.034437 0.045293 0.001232 0.053921 -0.096616 0.038282 0.045451 -0.067209 0.033871 -0.040518 -0.001177 -0.019998 0.013108 -0.037860 0.017715 -0.004965 0.021419 0.075541 -0.019197 -0.037451 
--0.093874 -0.016322 0.050427 0.038901 -0.035571 -0.013436 -0.053907 -0.038658 -0.029325 -0.036613 -0.032481 0.093868 -0.036396 0.021379 -0.092279 0.042691 0.039867 -0.062158 0.026548 -0.031466 0.003479 0.040316 0.006294 -0.036209 0.073025 0.000684 0.013187 0.010326 -0.008531 -0.005083 
--0.104244 -0.006709 0.067184 0.041289 -0.069241 0.012081 -0.075591 -0.089859 -0.002603 -0.023228 -0.014240 0.125765 -0.109929 0.003712 -0.029968 0.014433 -0.014119 -0.049355 0.046499 -0.085176 0.023721 0.068470 -0.048687 -0.048937 0.120813 0.007379 -0.041829 -0.122482 -0.067858 0.062213 
--0.098423 -0.011771 0.052601 0.020651 -0.034896 -0.021175 -0.065152 -0.049145 -0.028881 -0.037319 -0.039151 0.087044 -0.064168 0.042731 -0.067294 0.053022 0.023839 -0.042707 0.029993 -0.028916 0.011079 0.032455 0.013155 -0.006472 0.061777 0.012999 -0.006873 0.010614 -0.004050 -0.011204 
--0.086060 -0.021786 0.053949 0.013196 -0.031142 -0.043099 -0.085669 -0.038918 -0.044017 -0.027197 -0.045913 0.039160 -0.027725 0.076968 -0.067122 0.044150 0.021313 -0.039518 0.034774 -0.040416 0.000111 -0.028143 0.008737 -0.005983 0.003932 0.011395 0.001920 0.048028 0.008140 -0.040395 
-0.023234 -0.151224 -0.158262 0.044921 -0.009732 0.000281 -0.001484 -0.070081 -0.023992 0.004264 0.062205 -0.039427 0.038098 0.032554 0.055525 -0.066996 -0.164944 -0.122724 0.013346 0.030357 0.020003 -0.032163 0.019982 0.073629 0.021368 -0.071433 -0.018389 0.003734 -0.016513 0.043226 
--0.000995 -0.068801 -0.121336 0.072388 0.052784 -0.054867 0.032178 0.001244 -0.004833 -0.025828 0.009566 -0.015286 0.014651 0.010036 -0.015176 0.028930 -0.011269 0.006977 -0.027549 0.081052 -0.006338 -0.002885 -0.132402 0.005924 0.032534 -0.009681 0.028356 0.022311 0.035163 0.096200 
--0.004191 -0.024081 -0.071313 0.090010 0.069773 -0.094666 0.033894 0.052375 0.001162 -0.003099 -0.004738 -0.015529 -0.013298 -0.011804 -0.070290 0.037494 0.098463 0.091632 -0.087569 0.072527 -0.000020 -0.032255 -0.102761 -0.028732 0.010823 -0.026462 0.070892 -0.014064 0.023862 0.043866 
--0.015322 -0.024017 -0.054182 0.086743 0.060910 -0.101634 0.015786 0.057519 -0.002762 0.012999 0.000729 -0.023649 -0.035764 0.008028 -0.081935 0.022017 0.068587 0.090964 -0.058996 0.018408 -0.003382 -0.065054 -0.081165 -0.018146 0.054953 -0.029583 0.059280 -0.003811 -0.020414 0.000419 
--0.025819 -0.024102 -0.072656 0.090267 0.065909 -0.095620 0.028049 0.048384 0.020203 0.015879 0.003299 -0.016853 -0.024716 -0.011856 -0.098542 0.022346 0.054071 0.127108 -0.035058 0.033226 -0.018163 -0.039842 -0.075770 0.004476 0.119650 -0.037430 0.037285 -0.012838 -0.017586 -0.027302 
--0.028630 -0.075977 -0.114641 0.065261 0.041153 -0.056917 0.031445 -0.003049 0.034540 0.001182 0.018209 -0.030508 0.011536 -0.023990 -0.027811 0.007628 -0.058660 0.082643 0.021068 0.039513 -0.040660 -0.030634 -0.014500 0.008764 0.142474 0.046181 0.018696 -0.012111 0.041048 -0.020873 
--0.057340 -0.155110 -0.147834 0.043518 -0.030786 0.001235 -0.002459 -0.078828 0.037715 -0.015356 0.008971 -0.076546 0.039114 0.012417 0.102079 -0.023242 -0.198315 -0.056443 0.067516 -0.015237 -0.005601 -0.021764 0.080017 0.069763 0.034826 0.048933 -0.014850 -0.038689 0.040787 -0.048334 
--0.042943 -0.108650 -0.102366 0.023189 -0.010725 0.030229 0.068583 0.015128 0.022969 0.058778 -0.031171 -0.044495 -0.001699 -0.019406 0.036790 0.020578 -0.112566 0.009264 0.058955 -0.050722 0.000750 -0.006776 0.029042 -0.019202 0.064299 0.076581 -0.085961 0.008218 0.028521 -0.078822 
--0.035161 -0.066110 -0.032050 0.021658 0.003153 0.025066 0.099954 0.086178 0.006060 0.105083 -0.073476 -0.013431 -0.062933 -0.011137 -0.008463 0.044662 -0.041029 0.015288 0.018520 -0.085740 0.025027 -0.028340 0.005094 -0.045253 0.029209 0.031682 -0.151561 0.022658 -0.024863 -0.088980 
--0.017691 -0.069000 -0.017056 0.020545 -0.000723 0.010161 0.082301 0.100673 -0.018708 0.105667 -0.072642 -0.012585 -0.081805 0.028901 -0.017421 0.021910 -0.017603 -0.020619 0.017659 -0.091795 0.050986 -0.056081 -0.003138 -0.077547 -0.045882 0.045503 -0.131404 0.003944 -0.004661 -0.046965 
-0.000842 -0.069922 -0.031110 0.019580 0.004919 0.020637 0.094320 0.088857 -0.016319 0.096731 -0.065463 -0.006683 -0.059096 0.046883 -0.005018 0.008960 0.020725 -0.026722 -0.001947 -0.031727 0.042259 -0.000859 -0.026021 -0.109897 -0.102234 0.062551 -0.106192 -0.024631 0.031769 0.029184 
-0.011685 -0.107773 -0.105352 0.026528 0.003824 0.024236 0.066505 0.016072 -0.021701 0.046046 -0.022269 -0.019279 -0.010005 0.056022 0.031344 -0.000244 -0.043602 -0.069823 0.009639 0.014265 0.033485 0.010020 -0.039736 -0.064507 -0.051131 0.044598 -0.063073 0.001278 0.009848 0.098080 
-0.014081 -0.145232 -0.172859 0.038795 0.006394 0.024457 0.003527 -0.061601 -0.028767 0.008573 0.052014 -0.034604 0.029274 0.037979 0.039823 -0.056170 -0.173607 -0.116545 0.053799 -0.002652 -0.003032 -0.018566 0.074668 0.074705 0.084561 -0.070069 -0.017802 0.003303 -0.022098 -0.000798 
-0.004128 -0.026318 -0.048385 0.088799 0.062152 -0.062649 0.051018 0.040691 0.011967 0.000118 -0.006210 0.027329 -0.059771 0.047117 -0.071270 0.028075 0.115061 0.060203 -0.010964 0.084083 -0.064352 0.058946 -0.008933 -0.011132 0.010975 -0.099926 0.143810 -0.078143 -0.031049 0.024287 
--0.016065 -0.023834 -0.031338 0.088382 0.053203 -0.078391 0.033235 0.057767 -0.004620 0.017680 -0.013553 -0.001928 -0.066816 0.014141 -0.063354 0.027828 0.054090 0.072914 -0.031292 0.013275 -0.054482 -0.003537 -0.050452 -0.020547 -0.009186 -0.078005 0.078837 -0.026306 -0.062501 -0.051943 
--0.034367 -0.025663 -0.049240 0.086354 0.058280 -0.065883 0.062226 0.039226 0.009362 -0.006078 0.001675 0.021093 -0.052080 -0.010700 -0.047457 0.049514 0.054445 0.124857 -0.055582 0.029771 -0.060479 0.061865 -0.041608 -0.010528 -0.000334 -0.155128 0.113107 0.071186 -0.009910 -0.047118 
--0.048879 -0.151838 -0.160016 0.030692 -0.012296 0.024678 0.007708 -0.065576 0.022316 -0.018020 0.021758 -0.064162 0.034039 0.002657 0.090064 -0.018113 -0.190986 -0.088212 0.042825 -0.014056 0.001530 0.006546 0.076036 0.055804 -0.017103 0.049034 0.004591 -0.030501 0.052720 0.028802 
--0.038340 -0.063850 -0.059553 0.029051 0.047621 0.023365 0.105034 0.078003 0.000009 0.052377 -0.041481 0.029714 -0.078120 0.007371 -0.013626 0.050411 0.020254 0.052993 -0.010404 -0.057900 0.002028 0.046760 0.007269 -0.044843 -0.009151 -0.079503 0.034448 0.129386 0.043511 0.024506 
--0.017756 -0.059748 -0.036850 0.028881 0.045562 0.014200 0.079001 0.099353 -0.021063 0.074300 -0.049978 0.009019 -0.093130 0.029110 -0.031071 0.029463 0.021463 0.011127 0.023461 -0.071087 0.004178 -0.007604 0.014745 -0.065257 -0.024042 -0.019514 0.002369 0.025211 -0.003778 0.021779 
-0.004041 -0.065582 -0.057203 0.030295 0.051078 0.025285 0.094233 0.080360 -0.010665 0.060385 -0.038515 0.035509 -0.085684 0.065856 -0.038294 0.019563 0.077752 0.005082 0.039220 -0.005268 -0.007462 0.044494 0.050125 -0.050480 0.018069 -0.028114 0.067491 -0.029325 0.022104 0.094267 
-# The variances of the components (eigenvalues) of identity or combined identity and expression model
-1
-30
-6
-1097.284971 604.737272 330.509879 197.777415 158.903842 136.932869 98.869562 90.905660 77.964343 69.662131 64.452126 52.813284 46.277233 39.196537 36.560898 33.647765 27.169035 25.765754 24.865278 19.424782 17.704494 16.404349 15.949029 15.080271 12.805943 11.923468 10.033987 8.748427 8.551978 7.944364 
diff --git a/matlab_version/pdm_generation/menpo_pdm/split_menpo_data.m b/matlab_version/pdm_generation/menpo_pdm/split_menpo_data.m
index cb29fcd8..64c75839 100644
--- a/matlab_version/pdm_generation/menpo_pdm/split_menpo_data.m
+++ b/matlab_version/pdm_generation/menpo_pdm/split_menpo_data.m
@@ -1,34 +1,27 @@
-menpo_root = 'C:\Users\tbaltrus\Documents\menpo_data_orig/';
+% Script for internal menpo data split, convert the original training data
+% to 2/3rds training and 1/3rd validation
+
+menpo_root = 'D:\Datasets\menpo/';
 
 pts_files = dir([menpo_root, '/*.jpg']);
 jpg_files = dir([menpo_root, '/*.pts']);
 
-% Split the menpo data into training (2/3rds) and validation (1/3rd)
-rng(0);
-indices = randperm(numel(pts_files));
-train_ind = indices(1:round(2*numel(pts_files)/3));
-valid_ind = indices(round(2*numel(pts_files)/3)+1:end);
-
 % Do the actual copying to respective folders
 out_train = [menpo_root, '/train/'];
 out_valid = [menpo_root, '/valid/'];
 mkdir(out_train);
 mkdir(out_valid);
 
-train_imgs = jpg_files(train_ind);
-valid_imgs = jpg_files(valid_ind);
+load('train_valid_split');
 
-train_pts = pts_files(train_ind);
-valid_pts = pts_files(valid_ind);
-
-for i=1:numel(train_ind)
+for i=1:numel(train_imgs)
    
     copyfile([menpo_root, train_pts(i).name], [out_train, train_pts(i).name]);
     copyfile([menpo_root, train_imgs(i).name], [out_train, train_imgs(i).name]);
     
 end
 
-for i=1:numel(valid_ind)
+for i=1:numel(valid_imgs)
    
     copyfile([menpo_root, valid_pts(i).name], [out_valid, valid_pts(i).name]);
     copyfile([menpo_root, valid_imgs(i).name], [out_valid, valid_imgs(i).name]);
diff --git a/matlab_version/pdm_generation/menpo_pdm/train_valid_split.mat b/matlab_version/pdm_generation/menpo_pdm/train_valid_split.mat
new file mode 100644
index 00000000..5abd545f
Binary files /dev/null and b/matlab_version/pdm_generation/menpo_pdm/train_valid_split.mat differ
diff --git a/matlab_version/pdm_generation/menpo_pdm/wild_68_pts.mat b/matlab_version/pdm_generation/menpo_pdm/wild_68_pts.mat
new file mode 100644
index 00000000..39f0b195
Binary files /dev/null and b/matlab_version/pdm_generation/menpo_pdm/wild_68_pts.mat differ
diff --git a/matlab_version/pdm_generation/visualise_PDMs.m b/matlab_version/pdm_generation/visualise_PDMs.m
index 69ebe118..a7a2255e 100644
--- a/matlab_version/pdm_generation/visualise_PDMs.m
+++ b/matlab_version/pdm_generation/visualise_PDMs.m
@@ -3,14 +3,14 @@ load './Wild_data_pdm/pdm_68_aligned_wild.mat';
 load('tri_68.mat', 'T');
 
 % Visualise it
-visualisePDM(M, E, V, T, 3, 3)
+visualisePDM(M, E, V, T, 5, 5)
 
 %% Torresani visualisation of Menpo PDM
-load './menpo_pdm/pdm_68_aligned_wild.mat';
+load './menpo_pdm/pdm_68_aligned_menpo_v5.mat';
 load('tri_68.mat', 'T');
 
 % Visualise it
-visualisePDM(M, E, V, T, 3, 3)
+visualisePDM(M, E, V, T, 7, 7)
 %% The multi-pie PDM visualisation
 load './Wild_data_pdm/pdm_68_multi_pie.mat';
 load('tri_68.mat', 'T');