diff --git a/matlab_version/experiments_iccv_300w/Collect_wild_imgs.m b/matlab_version/experiments_300W/Collect_wild_imgs.m
similarity index 100%
rename from matlab_version/experiments_iccv_300w/Collect_wild_imgs.m
rename to matlab_version/experiments_300W/Collect_wild_imgs.m
diff --git a/matlab_version/experiments_300W/Display_ceclm_results_49.m b/matlab_version/experiments_300W/Display_ceclm_results_49.m
new file mode 100644
index 00000000..8dde3c21
--- /dev/null
+++ b/matlab_version/experiments_300W/Display_ceclm_results_49.m
@@ -0,0 +1,160 @@
+clear
+close all
+
+% First grab the numbers
+Extract_table_results_49;
+
+%% Visualize all the baselines on all 300W
+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;
+
+[error_x, error_y] = cummErrorCurve(cat(1, ceclm_error_comm, ceclm_error_ibug));
+plot(error_x, error_y, 'r', 'DisplayName', 'CE-CLM ', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(cat(1, clnf_error_comm, clnf_error_ibug));
+plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(cat(1, error_3ddfa_comm, error_3ddfa_ibug));
+plot(error_x, error_y, 'DisplayName', '3DDFA', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(cat(1, drmf_error_comm, drmf_error_ibug));
+plot(error_x, error_y, 'DisplayName', 'DRMF', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(cat(1, cfss_error_comm, cfss_error_ibug));
+plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(cat(1, sdm_error_comm, sdm_error_ibug));
+plot(error_x, error_y, 'DisplayName', 'SDM', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(cat(1, tcdcn_error_comm, tcdcn_error_ibug));
+plot(error_x, error_y, 'DisplayName', 'TCDCN', 'LineWidth',line_width);
+
+set(gca,'xtick',[0:0.01:0.1])
+xlim([0.01,0.07]);
+xlabel('IOD normalised MAE','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',50)
+
+% Make sure CE-CLM curve is on top
+[error_x, error_y] = cummErrorCurve(cat(1, ceclm_error_comm, ceclm_error_ibug));
+plot(error_x, error_y, 'r', 'LineWidth',line_width);
+
+print -dpdf results/ceclm-300W-49.pdf
+print -dpng results/ceclm-300W-49.png
+%% Visualize all the baselines on difficult subset of 300W
+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;
+
+[error_x, error_y] = cummErrorCurve(ceclm_error_ibug);
+plot(error_x, error_y, 'r', 'DisplayName', 'CE-CLM ', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(clnf_error_ibug);
+plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(error_3ddfa_ibug);
+plot(error_x, error_y, 'DisplayName', '3DDFA', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(drmf_error_ibug);
+plot(error_x, error_y, 'DisplayName', 'DRMF', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(cfss_error_ibug);
+plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(sdm_error_ibug);
+plot(error_x, error_y, 'DisplayName', 'SDM', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(tcdcn_error_ibug);
+plot(error_x, error_y, 'DisplayName', 'TCDCN', 'LineWidth',line_width);
+
+set(gca,'xtick',[0:0.01:0.1])
+xlim([0.02,0.09]);
+xlabel('IOD normalised MAE','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',50)
+
+% Make sure CE-CLM curve is on top
+[error_x, error_y] = cummErrorCurve(ceclm_error_ibug);
+plot(error_x, error_y, 'r', 'LineWidth',line_width);
+
+print -dpdf results/ceclm-300W-ibug-49.pdf
+print -dpng results/ceclm-300W-ibug-49.png
+
+%% Visualize all the baselines on difficult subset of 300W
+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;
+
+[error_x, error_y] = cummErrorCurve(ceclm_error_comm);
+plot(error_x, error_y, 'r', 'DisplayName', 'CE-CLM ', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(clnf_error_comm);
+plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(error_3ddfa_comm);
+plot(error_x, error_y, 'DisplayName', '3DDFA', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(drmf_error_comm);
+plot(error_x, error_y, 'DisplayName', 'DRMF', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(cfss_error_comm);
+plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(sdm_error_comm);
+plot(error_x, error_y, 'DisplayName', 'SDM', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(tcdcn_error_comm);
+plot(error_x, error_y, 'DisplayName', 'TCDCN', 'LineWidth',line_width);
+
+set(gca,'xtick',[0:0.01:0.1])
+xlim([0.01,0.07]);
+xlabel('IOD normalised MAE','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',50)
+
+% Make sure CE-CLM curve is on top
+[error_x, error_y] = cummErrorCurve(ceclm_error_comm);
+plot(error_x, error_y, 'r', 'LineWidth',line_width);
+
+print -dpdf results/ceclm-300W-comm-49.pdf
+print -dpng results/ceclm-300W-comm-49.png
diff --git a/matlab_version/experiments_300W/Display_ceclm_results_68.m b/matlab_version/experiments_300W/Display_ceclm_results_68.m
new file mode 100644
index 00000000..aa9bdf2b
--- /dev/null
+++ b/matlab_version/experiments_300W/Display_ceclm_results_68.m
@@ -0,0 +1,151 @@
+clear
+close all
+
+% First grab the numbers
+Extract_table_results_68;
+
+%% Visualize all the baselines on all 300W
+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;
+
+[error_x, error_y] = cummErrorCurve(cat(1, ceclm_error_comm, ceclm_error_ibug));
+plot(error_x, error_y, 'r', 'DisplayName', 'CE-CLM ', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(cat(1, clnf_error_comm, clnf_error_ibug));
+plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(cat(1, error_3ddfa_comm, error_3ddfa_ibug));
+plot(error_x, error_y, 'DisplayName', '3DDFA', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(cat(1, drmf_error_comm, drmf_error_ibug));
+plot(error_x, error_y, 'DisplayName', 'DRMF', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(cat(1, cfss_error_comm, cfss_error_ibug));
+plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(cat(1, tcdcn_error_comm, tcdcn_error_ibug));
+plot(error_x, error_y, 'DisplayName', 'TCDCN', 'LineWidth',line_width);
+
+set(gca,'xtick',[0:0.01:0.1])
+xlim([0.01,0.07]);
+xlabel('IOD normalised MAE','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',50)
+
+% Make sure CE-CLM curve is on top
+[error_x, error_y] = cummErrorCurve(cat(1, ceclm_error_comm, ceclm_error_ibug));
+plot(error_x, error_y, 'r', 'LineWidth',line_width);
+
+print -dpdf results/ceclm-300W-68.pdf
+print -dpng results/ceclm-300W-68.png
+%% Visualize all the baselines on difficult subset of 300W
+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;
+
+[error_x, error_y] = cummErrorCurve(ceclm_error_ibug);
+plot(error_x, error_y, 'r', 'DisplayName', 'CE-CLM ', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(clnf_error_ibug);
+plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(error_3ddfa_ibug);
+plot(error_x, error_y, 'DisplayName', '3DDFA', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(drmf_error_ibug);
+plot(error_x, error_y, 'DisplayName', 'DRMF', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(cfss_error_ibug);
+plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(tcdcn_error_ibug);
+plot(error_x, error_y, 'DisplayName', 'TCDCN', 'LineWidth',line_width);
+
+set(gca,'xtick',[0:0.01:0.1])
+xlim([0.02,0.09]);
+xlabel('IOD normalised MAE','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',50)
+
+% Make sure CE-CLM curve is on top
+[error_x, error_y] = cummErrorCurve(ceclm_error_ibug);
+plot(error_x, error_y, 'r', 'LineWidth',line_width);
+
+print -dpdf results/ceclm-300W-ibug-68.pdf
+print -dpng results/ceclm-300W-ibug-68.png
+
+%% Visualize all the baselines on difficult subset of 300W
+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;
+
+[error_x, error_y] = cummErrorCurve(ceclm_error_comm);
+plot(error_x, error_y, 'r', 'DisplayName', 'CE-CLM ', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(clnf_error_comm);
+plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(error_3ddfa_comm);
+plot(error_x, error_y, 'DisplayName', '3DDFA', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(drmf_error_comm);
+plot(error_x, error_y, 'DisplayName', 'DRMF', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(cfss_error_comm);
+plot(error_x, error_y, 'DisplayName', 'CFSS', 'LineWidth',line_width);
+
+[error_x, error_y] = cummErrorCurve(tcdcn_error_comm);
+plot(error_x, error_y, 'DisplayName', 'TCDCN', 'LineWidth',line_width);
+
+set(gca,'xtick',[0:0.01:0.1])
+xlim([0.01,0.07]);
+xlabel('IOD normalised MAE','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',50)
+
+% Make sure CE-CLM curve is on top
+[error_x, error_y] = cummErrorCurve(ceclm_error_comm);
+plot(error_x, error_y, 'r', 'LineWidth',line_width);
+
+print -dpdf results/ceclm-300W-comm-68.pdf
+print -dpng results/ceclm-300W-comm-68.png
diff --git a/matlab_version/experiments_300W/Extract_table_results_49.m b/matlab_version/experiments_300W/Extract_table_results_49.m
new file mode 100644
index 00000000..143134b2
--- /dev/null
+++ b/matlab_version/experiments_300W/Extract_table_results_49.m
@@ -0,0 +1,173 @@
+clear
+
+%% 
+inds_ibug = 562:696;
+
+load('./results/results_clnf.mat');
+labels = experiment.labels([1:60,62:64,66:end],:,:);
+shapes = experiment.shapes([1:60,62:64,66:end],:,:);
+labels = labels(18:end,:,:);
+shapes = shapes(18:end,:,:);
+
+clnf_error_ibug = compute_error( labels(:,:,inds_ibug),  shapes(:,:,inds_ibug) + 1.0);
+
+% load('results/results_wild_dclm_general_v4.mat');
+load('./results/results_ceclm_menpo.mat');
+
+labels = experiment.labels([1:60,62:64,66:end],:,:);
+shapes = experiment.shapes([1:60,62:64,66:end],:,:);
+labels = labels(18:end,:,:);
+shapes = shapes(18:end,:,:);
+
+ceclm_error_ibug = compute_error( labels(:,:,inds_ibug),  shapes(:,:,inds_ibug) + 1.0);
+
+load('results/zhu_wild.mat');
+labels_all = labels_all(18:end,:,:);
+shapes_all = shapes_all(18:end,:,:);
+
+tsm_error_ibug = compute_error(labels_all(:,:,inds_ibug), shapes_all(:,:,inds_ibug));
+
+load('results/CFAN_300W.mat');
+shapes_all = shapes_all([1:60,62:64,66:end],:,:);
+shapes_all = shapes_all(18:end,:,:);
+labels_all = labels_all([1:60,62:64,66:end],:,:);
+labels_all = labels_all(18:end,:,:);
+
+cfan_error_ibug = compute_error(labels_all(:,:,inds_ibug), shapes_all(:,:,inds_ibug)+1.0);
+
+load('./results/results_clm.mat');
+labels = experiment.labels([1:60,62:64,66:end],:,:);
+shapes = experiment.shapes([1:60,62:64,66:end],:,:);
+labels = labels(18:end,:,:);
+shapes = shapes(18:end,:,:);
+clm_error_ibug = compute_error( labels(:,:,inds_ibug),  shapes(:,:,inds_ibug) + 1.0);
+
+load('results/300W_3DDFA.mat');
+shapes = shapes([1:60,62:64,66:end],:,:);
+shapes = shapes(18:end,:,:);
+error_3ddfa_ibug = compute_error( labels_all(:,:,inds_ibug),  shapes(:,:,inds_ibug) + 0.5);
+
+load('results/drmf_wild.mat');
+labels_all = labels_all(18:end,:,:);
+shapes_all = shapes_all(18:end,:,:);
+
+drmf_error_ibug = compute_error(labels_all(:,:,inds_ibug), shapes_all(:,:,inds_ibug));
+
+load('results/300W_sdm.mat');
+shapes_all = experiments.shapes;
+
+sdm_error_ibug = compute_error(labels_all(:,:,inds_ibug), shapes_all(:,:,inds_ibug));
+
+load('results/300W_pocr.mat');
+shapes_all = experiments.shapes;
+
+po_cr_error_ibug = compute_error(labels_all(:,:,inds_ibug), shapes_all(:,:,inds_ibug) + 1.0);
+
+load('results/300W-CFSS.mat');
+
+shapes = zeros(size(estimatedPose,2)/2,2,size(estimatedPose,1));
+for i=1:size(estimatedPose,1)
+    xs = estimatedPose(i,1:end/2);
+    ys = estimatedPose(i,end/2+1:end);
+    shapes(:,1,i) = xs;
+    shapes(:,2,i) = ys;
+end
+shapes = shapes([1:60,62:64,66:end],:,:);
+shapes = shapes(18:end,:,:);
+
+cfss_error_ibug = compute_error(labels_all(:,:,inds_ibug), shapes(:,:,inds_ibug)+0.5);
+
+load('results/tcdcn_300W.mat');
+shapes_c = shapes;
+shapes = zeros(68,2,numel(shapes));
+for i=1:numel(shapes_c)
+    xs = shapes_c{i}(:,1);
+    ys = shapes_c{i}(:,2);
+    shapes(:,1,i) = xs;
+    shapes(:,2,i) = ys;
+end
+shapes = shapes([1:60,62:64,66:end],:,:);
+shapes = shapes(18:end,:,:);
+
+load('results/results_clnf.mat');
+tcdcn_error_ibug = compute_error(labels_all(:,:,inds_ibug), shapes(:,:,inds_ibug));
+
+%% 
+inds_comm = [338:561,697:1026];
+
+load('./results/results_clnf.mat');
+labels = experiment.labels([1:60,62:64,66:end],:,:);
+shapes = experiment.shapes([1:60,62:64,66:end],:,:);
+labels = labels(18:end,:,:);
+shapes = shapes(18:end,:,:);
+
+clnf_error_comm = compute_error( labels(:,:,inds_comm),  shapes(:,:,inds_comm) + 1.0);
+
+load('./results/results_ceclm_menpo.mat');
+labels = experiment.labels([1:60,62:64,66:end],:,:);
+shapes = experiment.shapes([1:60,62:64,66:end],:,:);
+labels = labels(18:end,:,:);
+shapes = shapes(18:end,:,:);
+
+ceclm_error_comm = compute_error( labels(:,:,inds_comm),  shapes(:,:,inds_comm) + 1.0);
+
+load('results/zhu_wild.mat');
+labels_all = labels_all(18:end,:,:);
+shapes_all = shapes_all(18:end,:,:);
+
+tsm_error_comm = compute_error(labels_all(:,:,inds_comm), shapes_all(:,:,inds_comm));
+
+load('./results/results_clm.mat');
+labels = experiment.labels([1:60,62:64,66:end],:,:);
+shapes = experiment.shapes([1:60,62:64,66:end],:,:);
+labels = labels(18:end,:,:);
+shapes = shapes(18:end,:,:);
+clm_error_comm = compute_error( labels(:,:,inds_comm),  shapes(:,:,inds_comm) + 1.0);
+
+load('results/300W_3DDFA.mat');
+shapes = shapes([1:60,62:64,66:end],:,:);
+shapes = shapes(18:end,:,:);
+error_3ddfa_comm = compute_error( labels_all(:,:,inds_comm),  shapes(:,:,inds_comm)+0.5);
+
+load('results/drmf_wild.mat');
+labels_all = labels_all(18:end,:,:);
+shapes_all = shapes_all(18:end,:,:);
+
+drmf_error_comm = compute_error(labels_all(:,:,inds_comm), shapes_all(:,:,inds_comm));
+
+load('results/300W_sdm.mat');
+shapes_all = experiments.shapes;
+
+sdm_error_comm = compute_error(labels_all(:,:,inds_comm), shapes_all(:,:,inds_comm));
+
+load('results/300W_pocr.mat');
+shapes_all = experiments.shapes;
+
+po_cr_error_comm = compute_error(labels_all(:,:,inds_comm), shapes_all(:,:,inds_comm) + 1.0);
+
+load('results/300W-CFSS.mat');
+
+shapes = zeros(size(estimatedPose,2)/2,2,size(estimatedPose,1));
+for i=1:size(estimatedPose,1)
+    xs = estimatedPose(i,1:end/2);
+    ys = estimatedPose(i,end/2+1:end);
+    shapes(:,1,i) = xs;
+    shapes(:,2,i) = ys;
+end
+shapes = shapes([1:60,62:64,66:end],:,:);
+shapes = shapes(18:end,:,:);
+
+cfss_error_comm = compute_error(labels_all(:,:,inds_comm), shapes(:,:,inds_comm)+0.5);
+
+load('results/tcdcn_300W.mat');
+shapes_c = shapes;
+shapes = zeros(68,2,numel(shapes));
+for i=1:numel(shapes_c)
+    xs = shapes_c{i}(:,1);
+    ys = shapes_c{i}(:,2);
+    shapes(:,1,i) = xs;
+    shapes(:,2,i) = ys;
+end
+shapes = shapes([1:60,62:64,66:end],:,:);
+shapes = shapes(18:end,:,:);
+tcdcn_error_comm = compute_error(labels_all(:,:,inds_comm), shapes(:,:,inds_comm));
\ No newline at end of file
diff --git a/matlab_version/experiments_300W/Extract_table_results_68.m b/matlab_version/experiments_300W/Extract_table_results_68.m
new file mode 100644
index 00000000..20b69770
--- /dev/null
+++ b/matlab_version/experiments_300W/Extract_table_results_68.m
@@ -0,0 +1,100 @@
+clear
+
+%% 
+inds_ibug = 562:696;
+
+load('results/results_clnf.mat');
+
+clnf_error_ibug = compute_error( experiment.labels(:,:,inds_ibug),  experiment.shapes(:,:,inds_ibug) + 1.0);
+
+load('./results/results_ceclm_general.mat');
+
+ceclm_error_ibug = compute_error( experiment.labels(:,:,inds_ibug),  experiment.shapes(:,:,inds_ibug) + 1.0);
+
+load('results/zhu_wild.mat');
+
+tsm_error_ibug = compute_error(labels_all(:,:,inds_ibug), shapes_all(:,:,inds_ibug));
+
+load('results/300W_3DDFA.mat');
+error_3ddfa_ibug = compute_error( labels_all(:,:,inds_ibug),  shapes(:,:,inds_ibug) + 0.5);
+
+load('results/results_clm.mat');
+clm_error_ibug = compute_error( experiment.labels(:,:,inds_ibug),  experiment.shapes(:,:,inds_ibug) + 1.0);
+
+load('results/drmf_wild.mat');
+
+drmf_error_ibug = compute_error(labels_all(:,:,inds_ibug), shapes_all(:,:,inds_ibug));
+
+load('results/CFAN_300W.mat');
+
+cfan_error_ibug = compute_error(labels_all(:,:,inds_ibug), shapes_all(:,:,inds_ibug)+1.0);
+
+load('results/300W-CFSS.mat');
+
+shapes = zeros(size(estimatedPose,2)/2,2,size(estimatedPose,1));
+for i=1:size(estimatedPose,1)
+    xs = estimatedPose(i,1:end/2);
+    ys = estimatedPose(i,end/2+1:end);
+    shapes(:,1,i) = xs;
+    shapes(:,2,i) = ys;
+end
+load('results/results_clnf.mat');
+cfss_error_ibug = compute_error(experiment.labels(:,:,inds_ibug), shapes(:,:,inds_ibug)+0.5);
+
+
+load('results/tcdcn_300W.mat');
+shapes_c = shapes;
+shapes = zeros(68,2,numel(shapes));
+for i=1:numel(shapes_c)
+    xs = shapes_c{i}(:,1);
+    ys = shapes_c{i}(:,2);
+    shapes(:,1,i) = xs;
+    shapes(:,2,i) = ys;
+end
+load('results/results_clnf.mat');
+tcdcn_error_ibug = compute_error(experiment.labels(:,:,inds_ibug), shapes(:,:,inds_ibug));
+
+%% 
+inds_comm = [338:561,697:1026];
+
+load('./results/results_clnf.mat');
+clnf_error_comm = compute_error( experiment.labels(:,:,inds_comm),  experiment.shapes(:,:,inds_comm) + 1.0);
+
+load('./results/results_ceclm_general.mat');
+ceclm_error_comm = compute_error( experiment.labels(:,:,inds_comm),  experiment.shapes(:,:,inds_comm) + 1.0);
+
+load('results/zhu_wild.mat');
+tsm_error_comm = compute_error(labels_all(:,:,inds_comm), shapes_all(:,:,inds_comm));
+
+load('results/results_clm.mat');
+clm_error_comm = compute_error( experiment.labels(:,:,inds_comm),  experiment.shapes(:,:,inds_comm) + 1.0);
+
+load('results/drmf_wild.mat');
+drmf_error_comm = compute_error(labels_all(:,:,inds_comm), shapes_all(:,:,inds_comm));
+
+load('results/300W_3DDFA.mat');
+error_3ddfa_comm = compute_error( labels_all(:,:,inds_comm),  shapes(:,:,inds_comm) + 0.5);
+
+load('results/300W-CFSS.mat');
+shapes = zeros(size(estimatedPose,2)/2,2,size(estimatedPose,1));
+for i=1:size(estimatedPose,1)
+    xs = estimatedPose(i,1:end/2);
+    ys = estimatedPose(i,end/2+1:end);
+    shapes(:,1,i) = xs;
+    shapes(:,2,i) = ys;
+end
+
+load('./results/results_clnf.mat');
+cfss_error_comm = compute_error(experiment.labels(:,:,inds_comm), shapes(:,:,inds_comm)+0.5);
+
+load('results/tcdcn_300W.mat');
+shapes_c = shapes;
+shapes = zeros(68,2,numel(shapes));
+for i=1:numel(shapes_c)
+    xs = shapes_c{i}(:,1);
+    ys = shapes_c{i}(:,2);
+    shapes(:,1,i) = xs;
+    shapes(:,2,i) = ys;
+end
+load('./results/results_clnf.mat');
+tcdcn_error_comm = compute_error(experiment.labels(:,:,inds_comm), shapes(:,:,inds_comm));
diff --git a/matlab_version/experiments_300W/Script_CECLM_general.m b/matlab_version/experiments_300W/Script_CECLM_general.m
new file mode 100644
index 00000000..8be7765c
--- /dev/null
+++ b/matlab_version/experiments_300W/Script_CECLM_general.m
@@ -0,0 +1,109 @@
+function Script_CECLM_general()
+
+addpath(genpath('../'));
+
+% Replace this with the location of the 300W data location
+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 CE-CLM model and parameters   
+[patches, pdm, clmParams, early_term_params] = Load_CECLM_general();
+% Use the multi-hypothesis model, as bounding box tells nothing about
+% orientation
+multi_view = true;
+
+%% Setup recording
+experiment.params = clmParams;
+
+num_points = numel(pdm.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);
+
+% Change if you want to visualize the outputs
+verbose = false;
+output_img = false;
+
+if(output_img)
+    output_root = './ceclm_gen_out/';
+    if(~exist(output_root, 'dir'))
+        mkdir(output_root);
+    end
+end
+if(verbose)
+    f = figure;
+end
+
+
+%% Fitting the model to the provided images
+
+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; 0,30,0; 0,0,30; 0,0,-30;];
+        views = views * pi/180;                                                                                     
+        
+        [shape,~,~,lhood,lmark_lhood,view_used] =...
+            Fitting_from_bb_multi_hyp(image, [], bbox, pdm, patches, clmParams, views, early_term_params);
+    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(output_img)
+        v_points = sum(squeeze(labels(i,:,:)),2) > 0;
+        DrawFaceOnImg(image_orig, shape, sprintf('%s/%s%d.jpg', output_root, 'fit', i), bbox, v_points);
+    end
+    
+    if(verbose)
+        v_points = sum(squeeze(labels(i,:,:)),2) > 0;
+        DrawFaceOnFig(image_orig, shape, bbox, v_points);
+    end
+end
+toc
+
+experiment.errors_normed = compute_error(labels_all, shapes_all + 1.0);
+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;
+
+fprintf('Done: mean normed error %.3f median normed error %.4f\n', ...
+    mean(experiment.errors_normed), median(experiment.errors_normed));
+
+%%
+output_results = 'results/results_ceclm_general.mat';
+save(output_results, 'experiment');
+    
+end
diff --git a/matlab_version/experiments_300W/Script_CECLM_menpo.m b/matlab_version/experiments_300W/Script_CECLM_menpo.m
new file mode 100644
index 00000000..90427a75
--- /dev/null
+++ b/matlab_version/experiments_300W/Script_CECLM_menpo.m
@@ -0,0 +1,109 @@
+function Script_CECLM_menpo()
+
+addpath(genpath('../'));
+
+% Replace this with the location of the 300W data location
+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 CE-CLM model and parameters   
+[patches, pdm, clmParams, early_term_params] = Load_CECLM_menpo();
+% Use the multi-hypothesis model, as bounding box tells nothing about
+% orientation
+multi_view = true;
+
+%% Setup recording
+experiment.params = clmParams;
+
+num_points = numel(pdm.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);
+
+% Change if you want to visualize the outputs
+verbose = false;
+output_img = false;
+
+if(output_img)
+    output_root = './ceclm_menpo_out/';
+    if(~exist(output_root, 'dir'))
+        mkdir(output_root);
+    end
+end
+if(verbose)
+    f = figure;
+end
+
+
+%% Fitting the model to the provided images
+
+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; 0,30,0; 0,0,30; 0,0,-30;];
+        views = views * pi/180;                                                                                     
+        
+        [shape,~,~,lhood,lmark_lhood,view_used] =...
+            Fitting_from_bb_multi_hyp(image, [], bbox, pdm, patches, clmParams, views, early_term_params);
+    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(output_img)
+        v_points = sum(squeeze(labels(i,:,:)),2) > 0;
+        DrawFaceOnImg(image_orig, shape, sprintf('%s/%s%d.jpg', output_root, 'fit', i), bbox, v_points);
+    end
+    
+    if(verbose)
+        v_points = sum(squeeze(labels(i,:,:)),2) > 0;
+        DrawFaceOnFig(image_orig, shape, bbox, v_points);
+    end
+end
+toc
+
+experiment.errors_normed = compute_error(labels_all, shapes_all + 1.0);
+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;
+
+fprintf('Done: mean normed error %.3f median normed error %.4f\n', ...
+    mean(experiment.errors_normed), median(experiment.errors_normed));
+
+%%
+output_results = 'results/results_ceclm_menpo.mat';
+save(output_results, 'experiment');
+    
+end
diff --git a/matlab_version/experiments_iccv_300w/Script_CLM_wild_iccv.m b/matlab_version/experiments_300W/Script_CLM.m
similarity index 57%
rename from matlab_version/experiments_iccv_300w/Script_CLM_wild_iccv.m
rename to matlab_version/experiments_300W/Script_CLM.m
index 367269c6..86b91e1a 100644
--- a/matlab_version/experiments_iccv_300w/Script_CLM_wild_iccv.m
+++ b/matlab_version/experiments_300W/Script_CLM.m
@@ -1,15 +1,12 @@
-function Script_CLM_wild_iccv()
+function Script_CLM()
 
-addpath('../PDM_helpers/');
-addpath('../fitting/normxcorr2_mex_ALL');
-addpath('../fitting/');
-addpath('../models/');
+addpath(genpath('../'));
 
 % Replace this with the location of in 300 faces in the wild data
 if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
     root_test_data = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];    
 else
-    root_test_data = 'F:/Dropbox/Dropbox/AAM/test data/';
+    root_test_data = 'D:/Dropbox/Dropbox/AAM/test data/';
 end
 
 [images, detections, labels] = Collect_wild_imgs(root_test_data);
@@ -25,8 +22,19 @@ clmParams.numPatchIters = size(clmParams.window_size,1);
 
 %% Fitting the model to the provided image
 
-verbose = false; % set to true to visualise the fitting
-output_root = './wild_fit_clm/';
+% Change if you want to visualize the outputs
+verbose = false;
+output_img = false;
+
+if(output_img)
+    output_root = './clm_out/';
+    if(~exist(output_root, 'dir'))
+        mkdir(output_root);
+    end
+end
+if(verbose)
+    f = figure;
+end
 
 % the default PDM to use
 pdmLoc = ['../models/pdm/pdm_68_aligned_wild.mat'];
@@ -79,7 +87,7 @@ for i=1:numel(images)
     % 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 = [0,0,0; 0,-30,0; 0,30,0; 0,0,30; 0,0,-30;];
         views = views * pi/180;                                                                                     
 
         shapes = zeros(num_points, 2, size(views,1));
@@ -113,72 +121,30 @@ for i=1:numel(images)
     end
         
     lhoods(i) = lhood;
-    if(verbose)
-        % Center the pixel
-        actualShape = squeeze(labels(i,:,:)) - 0.5;
-
-        [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)
+    
+    if(output_img)
         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
+        DrawFaceOnImg(image_orig, shape, sprintf('%s/%s%d.jpg', output_root, 'fit', i), bbox, v_points);
+    end
+    
+    if(verbose)
+        v_points = sum(squeeze(labels(i,:,:)),2) > 0;
+        DrawFaceOnFig(image_orig, shape, bbox, v_points);
     end
 
 end
 toc
-experiment.errors_normed = compute_error(labels_all - 0.5, shapes_all);
+experiment.errors_normed = compute_error(labels_all, shapes_all + 1.0);
 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));
 
+fprintf('Done: mean normed error %.3f median normed error %.4f\n', ...
+    mean(experiment.errors_normed), median(experiment.errors_normed));
 %%
-output_results = 'results/results_wild_clm.mat';
-save(output_results, 'experiments');
+output_results = 'results/results_clm.mat';
+save(output_results, 'experiment');
     
 end
diff --git a/matlab_version/experiments_in_the_wild/Script_CLNF_general_no_out.m b/matlab_version/experiments_300W/Script_CLNF.m
similarity index 59%
rename from matlab_version/experiments_in_the_wild/Script_CLNF_general_no_out.m
rename to matlab_version/experiments_300W/Script_CLNF.m
index d58e94a3..064109ed 100644
--- a/matlab_version/experiments_in_the_wild/Script_CLNF_general_no_out.m
+++ b/matlab_version/experiments_300W/Script_CLNF.m
@@ -1,10 +1,6 @@
-function Script_CLNF_general_no_out()
+function Script_CLNF()
 
-addpath('../PDM_helpers/');
-addpath('../fitting/normxcorr2_mex_ALL');
-addpath('../fitting/');
-addpath('../CCNF/');
-addpath('../models/');
+addpath(genpath('../'));
 
 % Replace this with the location of in 300 faces in the wild data
 if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
@@ -14,20 +10,14 @@ else
 end
 
 [images, detections, labels] = Collect_wild_imgs(root_test_data);
-
-%% loading the patch experts
+%% loading the patch experts and pdms
    
 clmParams = struct;
 
 clmParams.window_size = [25,25; 23,23; 21,21];
 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
-
-verbose = true; % set to true to visualise the fitting
-output_root = './wild_fit_clnf/';
+[patches] = Load_Patch_Experts( '../models/wild/', 'ccnf_patches_*_wild.mat', [], [], clmParams);
 
 % the default PDM to use
 pdmLoc = ['../models/pdm/pdm_68_aligned_wild.mat'];
@@ -57,12 +47,24 @@ inds_inner = 18:68;
 [patches_inner] = Load_Patch_Experts( '../models/general/', 'ccnf_patches_*general_no_out.mat', [], [], clmParams_inner);
 clmParams_inner.multi_modal_types  = patches_inner(1).multi_modal_types;
 
-load('results/results_wild_clnf_general.mat');
-clear 'experiments';
-
 % for recording purposes
 experiment.params = clmParams;
 
+%% Change if you want to visualize the outputs
+verbose = false;
+output_img = false;
+
+if(output_img)
+    output_root = './clnf_out/';
+    if(~exist(output_root, 'dir'))
+        mkdir(output_root);
+    end
+end
+if(verbose)
+    f = figure;
+end
+
+%% For recording
 num_points = numel(M)/3;
 
 shapes_all = zeros(size(labels,2),size(labels,3), size(labels,1));
@@ -75,6 +77,8 @@ all_views_used = zeros(numel(images),1);
 % orientation
 multi_view = true;
 
+%% Fitting the model to the provided image
+
 tic
 for i=1:numel(images)
 
@@ -90,7 +94,7 @@ for i=1:numel(images)
     % 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 = [0,0,0; 0,-30,0; 0,30,0; 0,0,30; 0,0,-30;];
         views = views * pi/180;                                                                                     
 
         shapes = zeros(num_points, 2, size(views,1));
@@ -120,9 +124,9 @@ for i=1:numel(images)
     if(a > 0.9)
         g_param = [a; Rot2Euler(R)'; T];
 
-        bbox = [min(shape_inner(:,1)), min(shape_inner(:,2)), max(shape_inner(:,1)), max(shape_inner(:,2))];
+        bbox_2 = [min(shape_inner(:,1)), min(shape_inner(:,2)), max(shape_inner(:,1)), max(shape_inner(:,2))];
 
-        [shape_inner] = Fitting_from_bb(image, [], bbox, pdm_inner, patches_inner, clmParams_inner, 'gparam', g_param, 'lparam', l_params);
+        [shape_inner] = Fitting_from_bb(image, [], bbox_2, pdm_inner, patches_inner, clmParams_inner, 'gparam', g_param, 'lparam', l_params);
 
         % Now after detections incorporate the eyes back
         % into the face model
@@ -146,72 +150,32 @@ for i=1:numel(images)
 
     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)
+    
+    if(output_img)
         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
+        DrawFaceOnImg(image_orig, shape, sprintf('%s/%s%d.jpg', output_root, 'fit', i), bbox, v_points);
+    end
+    
+    if(verbose)
+        v_points = sum(squeeze(labels(i,:,:)),2) > 0;
+        DrawFaceOnFig(image_orig, shape, bbox, v_points);
     end
 
 end
 toc
 
-experiment.errors_normed = compute_error(labels_all - 0.5, shapes_all);
+experiment.errors_normed = compute_error(labels_all, shapes_all + 1.0);
 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));
+
+fprintf('Done: mean normed error %.3f median normed error %.4f\n', ...
+    mean(experiment.errors_normed), median(experiment.errors_normed));
 
 %%
-output_results = 'results/results_wild_clnf_general_final_inner.mat';
-save(output_results, 'experiments');
+output_results = 'results/results_clnf.mat';
+save(output_results, 'experiment');
     
 end
diff --git a/matlab_version/experiments_iccv_300w/comp_error_pts.m b/matlab_version/experiments_300W/comp_error_pts.m
similarity index 100%
rename from matlab_version/experiments_iccv_300w/comp_error_pts.m
rename to matlab_version/experiments_300W/comp_error_pts.m
diff --git a/matlab_version/experiments_iccv_300w/compute_error.m b/matlab_version/experiments_300W/compute_error.m
similarity index 100%
rename from matlab_version/experiments_iccv_300w/compute_error.m
rename to matlab_version/experiments_300W/compute_error.m
diff --git a/matlab_version/experiments_iccv_300w/cummErrorCurve.m b/matlab_version/experiments_300W/cummErrorCurve.m
similarity index 100%
rename from matlab_version/experiments_iccv_300w/cummErrorCurve.m
rename to matlab_version/experiments_300W/cummErrorCurve.m
diff --git a/matlab_version/experiments_iccv_300w/results/300W-CFSS.mat b/matlab_version/experiments_300W/results/300W-CFSS.mat
similarity index 100%
rename from matlab_version/experiments_iccv_300w/results/300W-CFSS.mat
rename to matlab_version/experiments_300W/results/300W-CFSS.mat
diff --git a/matlab_version/experiments_300W/results/300W_3DDFA.mat b/matlab_version/experiments_300W/results/300W_3DDFA.mat
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diff --git a/matlab_version/experiments_iccv_300w/results/300W_pocr.mat b/matlab_version/experiments_300W/results/300W_pocr.mat
similarity index 100%
rename from matlab_version/experiments_iccv_300w/results/300W_pocr.mat
rename to matlab_version/experiments_300W/results/300W_pocr.mat
diff --git a/matlab_version/experiments_iccv_300w/results/300W_sdm.mat b/matlab_version/experiments_300W/results/300W_sdm.mat
similarity index 100%
rename from matlab_version/experiments_iccv_300w/results/300W_sdm.mat
rename to matlab_version/experiments_300W/results/300W_sdm.mat
diff --git a/matlab_version/experiments_300W/results/CFAN_300W.mat b/matlab_version/experiments_300W/results/CFAN_300W.mat
new file mode 100644
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diff --git a/matlab_version/experiments_iccv_300w/results/GNDPM_300W.mat b/matlab_version/experiments_300W/results/GNDPM_300W.mat
similarity index 100%
rename from matlab_version/experiments_iccv_300w/results/GNDPM_300W.mat
rename to matlab_version/experiments_300W/results/GNDPM_300W.mat
diff --git a/matlab_version/experiments_300W/results/ceclm-300W-49.pdf b/matlab_version/experiments_300W/results/ceclm-300W-49.pdf
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diff --git a/matlab_version/experiments_iccv_300w/results/drmf_wild.mat b/matlab_version/experiments_300W/results/drmf_wild.mat
similarity index 100%
rename from matlab_version/experiments_iccv_300w/results/drmf_wild.mat
rename to matlab_version/experiments_300W/results/drmf_wild.mat
diff --git a/matlab_version/experiments_300W/results/results_ceclm_general.mat b/matlab_version/experiments_300W/results/results_ceclm_general.mat
new file mode 100644
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new file mode 100644
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diff --git a/matlab_version/experiments_iccv_300w/results/results_wild_clm.mat b/matlab_version/experiments_300W/results/results_clm.mat
similarity index 50%
rename from matlab_version/experiments_iccv_300w/results/results_wild_clm.mat
rename to matlab_version/experiments_300W/results/results_clm.mat
index b8133a71..f581164c 100644
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diff --git a/matlab_version/experiments_in_the_wild/results/results_wild_clnf_general_final_inner.mat b/matlab_version/experiments_300W/results/results_clnf.mat
similarity index 50%
rename from matlab_version/experiments_in_the_wild/results/results_wild_clnf_general_final_inner.mat
rename to matlab_version/experiments_300W/results/results_clnf.mat
index f102d978..6a37e2ff 100644
Binary files a/matlab_version/experiments_in_the_wild/results/results_wild_clnf_general_final_inner.mat and b/matlab_version/experiments_300W/results/results_clnf.mat differ
diff --git a/matlab_version/experiments_300W/results/tcdcn_300W.mat b/matlab_version/experiments_300W/results/tcdcn_300W.mat
new file mode 100644
index 00000000..6ba01e14
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diff --git a/matlab_version/experiments_iccv_300w/results/yu_wild.mat b/matlab_version/experiments_300W/results/yu_wild.mat
similarity index 100%
rename from matlab_version/experiments_iccv_300w/results/yu_wild.mat
rename to matlab_version/experiments_300W/results/yu_wild.mat
diff --git a/matlab_version/experiments_iccv_300w/results/zhu_wild.mat b/matlab_version/experiments_300W/results/zhu_wild.mat
similarity index 100%
rename from matlab_version/experiments_iccv_300w/results/zhu_wild.mat
rename to matlab_version/experiments_300W/results/zhu_wild.mat
diff --git a/matlab_version/experiments_JANUS/Extract_table_results_49.m b/matlab_version/experiments_JANUS/Extract_table_results_49.m
index 86a2cf6b..4d49cde5 100644
--- a/matlab_version/experiments_JANUS/Extract_table_results_49.m
+++ b/matlab_version/experiments_JANUS/Extract_table_results_49.m
@@ -1,6 +1,6 @@
 clear
 
-load('results/results_wild_clnf_general.mat');
+load('results/results_clnf_wild.mat');
 
 [clnf_error, ~,~,frontal_ids] = compute_error_small( experiment.labels,  experiment.shapes-1.0);
 clnf_error_frontal = clnf_error(frontal_ids);
diff --git a/matlab_version/experiments_JANUS/Extract_table_results_68.m b/matlab_version/experiments_JANUS/Extract_table_results_68.m
index 3c3cdcfe..f716ec65 100644
--- a/matlab_version/experiments_JANUS/Extract_table_results_68.m
+++ b/matlab_version/experiments_JANUS/Extract_table_results_68.m
@@ -1,6 +1,6 @@
 clear
 
-load('results/results_wild_clnf_general.mat');
+load('results/results_clnf_wild.mat');
 
 [clnf_error, ~,~,frontal_ids] = compute_error( experiment.labels,  experiment.shapes-1.0);
 clnf_error_frontal = clnf_error(frontal_ids);
diff --git a/matlab_version/experiments_JANUS/Script_CECLM_menpo.m b/matlab_version/experiments_JANUS/Script_CECLM_menpo.m
index e6555ffa..e8253493 100644
--- a/matlab_version/experiments_JANUS/Script_CECLM_menpo.m
+++ b/matlab_version/experiments_JANUS/Script_CECLM_menpo.m
@@ -28,7 +28,7 @@ verbose = false;
 output_img = false;
 
 if(output_img)
-    output_root = './ceclm_gen_out/';
+    output_root = './ceclm_menpo_out/';
     if(~exist(output_root, 'dir'))
         mkdir(output_root);
     end
diff --git a/matlab_version/experiments_JANUS/Script_CLNF_general.m b/matlab_version/experiments_JANUS/Script_CLNF_general.m
index e91508ed..3f576760 100644
--- a/matlab_version/experiments_JANUS/Script_CLNF_general.m
+++ b/matlab_version/experiments_JANUS/Script_CLNF_general.m
@@ -13,7 +13,7 @@ clmParams = struct;
 clmParams.window_size = [25,25; 23,23; 21,21];
 clmParams.numPatchIters = size(clmParams.window_size,1);
 
-[patches] = Load_Patch_Experts( '../models/general/', 'ccnf_patches_*_general.mat', [], [], clmParams);
+[patches] = Load_Patch_Experts( '../models/wild/', 'ccnf_patches_*_wild.mat', [], [], clmParams);
 
 %% Fitting the model to the provided image
 
@@ -106,9 +106,9 @@ for i=1:numel(images)
     if(a > 0.9)
         g_param = [a; Rot2Euler(R)'; T];
 
-        bbox = [min(shape_inner(:,1)), min(shape_inner(:,2)), max(shape_inner(:,1)), max(shape_inner(:,2))];
+        bbox_2 = [min(shape_inner(:,1)), min(shape_inner(:,2)), max(shape_inner(:,1)), max(shape_inner(:,2))];
 
-        [shape_inner] = Fitting_from_bb(image, [], bbox, pdm_inner, patches_inner, clmParams_inner, 'gparam', g_param, 'lparam', l_params);
+        [shape_inner] = Fitting_from_bb(image, [], bbox_2, pdm_inner, patches_inner, clmParams_inner, 'gparam', g_param, 'lparam', l_params);
 
         % Now after detections incorporate the eyes back
         % into the face model
@@ -151,7 +151,7 @@ fprintf('Done: mean normed error %.3f median normed error %.4f\n', ...
     mean(experiment.errors_normed), median(experiment.errors_normed));
 
 %%
-output_results = 'results/results_wild_clnf_general.mat';
+output_results = 'results/results_clnf_wild.mat';
 save(output_results, 'experiment');
     
 end
diff --git a/matlab_version/experiments_JANUS/results/Janus-full.pdf b/matlab_version/experiments_JANUS/results/Janus-full.pdf
index 368c630b..c5e88702 100644
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diff --git a/matlab_version/experiments_JANUS/results/Janus-full.png b/matlab_version/experiments_JANUS/results/Janus-full.png
index 532746cd..de54b75d 100644
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diff --git a/matlab_version/experiments_JANUS/results/Janus-no-outline.pdf b/matlab_version/experiments_JANUS/results/Janus-no-outline.pdf
index c629a240..d790d8b9 100644
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diff --git a/matlab_version/experiments_JANUS/results/Janus-no-outline.png b/matlab_version/experiments_JANUS/results/Janus-no-outline.png
index e0a0a10e..a30cbdf2 100644
Binary files a/matlab_version/experiments_JANUS/results/Janus-no-outline.png and b/matlab_version/experiments_JANUS/results/Janus-no-outline.png differ
diff --git a/matlab_version/experiments_JANUS/results/results_clnf_wild.mat b/matlab_version/experiments_JANUS/results/results_clnf_wild.mat
new file mode 100644
index 00000000..882e8843
Binary files /dev/null and b/matlab_version/experiments_JANUS/results/results_clnf_wild.mat differ
diff --git a/matlab_version/experiments_JANUS/results/results_wild_clnf_general.mat b/matlab_version/experiments_JANUS/results/results_wild_clnf_general.mat
deleted file mode 100644
index f0541f2f..00000000
Binary files a/matlab_version/experiments_JANUS/results/results_wild_clnf_general.mat and /dev/null differ
diff --git a/matlab_version/experiments_iccv_300w/Display_clnf_results.m b/matlab_version/experiments_iccv_300w/Display_clnf_results.m
deleted file mode 100644
index 522e8a42..00000000
--- a/matlab_version/experiments_iccv_300w/Display_clnf_results.m
+++ /dev/null
@@ -1,181 +0,0 @@
-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/intraface_wild_resize.mat');
-labels_all = labels_all(18:end,:,:);
-shapes_all = shapes_all(18:end,:,:);
-
-intraface_wild_error = compute_error(labels_all, shapes_all);
-
-% removing faces that were not detected by intraface for fairness
-detected = intraface_wild_error < 1;
-
-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, 'r','DisplayName', 'OpenFace', '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',[0:0.05:0.15])
-xlim([0,0.08]);
-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');
-
-leg = legend('show', 'Location', 'SouthEast');
-set(leg,'FontSize',30)
-
-print -dpdf results/in-the-wild-clnf-no-outline.pdf
-
-%% 
-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],:,:);
-% 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
diff --git a/matlab_version/experiments_iccv_300w/Display_clnf_results_per_db.m b/matlab_version/experiments_iccv_300w/Display_clnf_results_per_db.m
deleted file mode 100644
index c400c60c..00000000
--- a/matlab_version/experiments_iccv_300w/Display_clnf_results_per_db.m
+++ /dev/null
@@ -1,246 +0,0 @@
-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('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
diff --git a/matlab_version/experiments_iccv_300w/Display_dclm_results.m b/matlab_version/experiments_iccv_300w/Display_dclm_results.m
deleted file mode 100644
index b8bd86ec..00000000
--- a/matlab_version/experiments_iccv_300w/Display_dclm_results.m
+++ /dev/null
@@ -1,246 +0,0 @@
-clear
-
-inds = 338:1026;
-%% 
-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('../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;
-
-dclm_error = compute_error( labels,  shapes);
-
-[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],:,:);
-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(inds));
-
-plot(error_x, error_y, 'DisplayName', 'CLNF', 'LineWidth',line_width);
-hold on;
-
-load('results/300W_pocr.mat');
-
-% center the pixel
-shapes_all = experiments.shapes;
-
-pocr_error = compute_error(labels, shapes_all(:,:,:) + 1.0);
-
-[error_x, error_y] = cummErrorCurve(pocr_error(inds));
-
-plot(error_x, error_y, 'DisplayName', 'PO-CR', 'LineWidth',line_width);
-hold on;
-
-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
-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(inds));
-
-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(:,:,:) + 1;
-labels_all = labels_all(:,:,:);
-
-gndpm_wild_error = compute_error(labels_all, shapes_all);
-
-[error_x, error_y] = cummErrorCurve(gndpm_wild_error(inds));
-
-plot(error_x, error_y, 'DisplayName', 'GNDPM', 'LineWidth',line_width);
-hold on;
-
-
-load('results/zhu_wild.mat');
-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(inds));
-
-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],:,:);
-% 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,:,:);
-shapes_all = shapes_all(18:end,:,:);
-
-drmf_error = compute_error(labels_all, shapes_all);
-
-[error_x, error_y] = cummErrorCurve(drmf_error(inds));
-
-plot(error_x, error_y, 'k','DisplayName', 'DRMF (CVPR 13)', 'LineWidth',line_width);
-
-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
-%% 
-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('../experiments_in_the_wild/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],:,:) + 0.5;
-
-dclm_error = compute_error( labels,  shapes);
-
-[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', 'Tree based', '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(inds));
-% 
-% 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(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(inds));
-
-plot(error_x, error_y, 'k','DisplayName', 'DRMF', 'LineWidth',line_width);
-
-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');
-
-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
\ No newline at end of file
diff --git a/matlab_version/experiments_iccv_300w/Display_dlm_results_per_db.m b/matlab_version/experiments_iccv_300w/Display_dlm_results_per_db.m
deleted file mode 100644
index 31cdd7c2..00000000
--- a/matlab_version/experiments_iccv_300w/Display_dlm_results_per_db.m
+++ /dev/null
@@ -1,258 +0,0 @@
-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
diff --git a/matlab_version/experiments_iccv_300w/Script_CFSS_vis.m b/matlab_version/experiments_iccv_300w/Script_CFSS_vis.m
deleted file mode 100644
index 99ee8b5b..00000000
--- a/matlab_version/experiments_iccv_300w/Script_CFSS_vis.m
+++ /dev/null
@@ -1,81 +0,0 @@
-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
\ No newline at end of file
diff --git a/matlab_version/experiments_iccv_300w/Script_CLNF_vis.m b/matlab_version/experiments_iccv_300w/Script_CLNF_vis.m
deleted file mode 100644
index f34a84ed..00000000
--- a/matlab_version/experiments_iccv_300w/Script_CLNF_vis.m
+++ /dev/null
@@ -1,80 +0,0 @@
-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
\ No newline at end of file
diff --git a/matlab_version/experiments_iccv_300w/Script_CLNF_wild_iccv.m b/matlab_version/experiments_iccv_300w/Script_CLNF_wild_iccv.m
deleted file mode 100644
index 2c81b6db..00000000
--- a/matlab_version/experiments_iccv_300w/Script_CLNF_wild_iccv.m
+++ /dev/null
@@ -1,188 +0,0 @@
-function Script_CLNF_wild_iccv()
-
-addpath('../PDM_helpers/');
-addpath('../fitting/normxcorr2_mex_ALL');
-addpath('../fitting/');
-addpath('../CCNF/');
-addpath('../models/');
-
-% Replace this with the location of in 300 faces in the wild data
-if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
-    root_test_data = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];    
-else
-    root_test_data = 'F:/Dropbox/Dropbox/AAM/test data/';
-end
-
-% load the images to detect landmarks of
-[images, detections, labels] = Collect_wild_imgs(root_test_data);
-
-%% loading the patch experts
-   
-clmParams = struct;
-
-clmParams.window_size = [25,25; 23,23; 21,21; 19,19;];
-
-clmParams.numPatchIters = size(clmParams.window_size,1);
-
-[patches] = Load_Patch_Experts( '../models/wild/', 'ccnf_patches_*_wild.mat', [], [], clmParams);
-
-%% Fitting the model to the provided image
-
-verbose = false; % set to true to visualise the fitting
-output_root = './wild_fit_clnf/';
-
-% 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);
-
-% the default model parameters to use
-clmParams.regFactor = [35, 27, 20, 5];
-clmParams.sigmaMeanShift = [1.25, 1.375, 1.5, 1.75]; 
-clmParams.tikhonov_factor = [2.5, 5, 7.5, 12.5];
-
-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;
-   
-% 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;
-
-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;
-
-    % shape correction for matlab format
-    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)
-        % Center the pixel
-        actualShape = squeeze(labels(i,:,:)) - 0.5;
-
-        [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_clnf.mat';
-save(output_results, 'experiments');
-    
-end
diff --git a/matlab_version/experiments_iccv_300w/Script_DCLM_vis.m b/matlab_version/experiments_iccv_300w/Script_DCLM_vis.m
deleted file mode 100644
index 22ce33ad..00000000
--- a/matlab_version/experiments_iccv_300w/Script_DCLM_vis.m
+++ /dev/null
@@ -1,80 +0,0 @@
-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
\ No newline at end of file
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diff --git a/matlab_version/experiments_iccv_300w/results/results_wild_clnf.mat b/matlab_version/experiments_iccv_300w/results/results_wild_clnf.mat
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diff --git a/matlab_version/experiments_in_the_wild/Collect_wild_imgs.m b/matlab_version/experiments_in_the_wild/Collect_wild_imgs.m
deleted file mode 100644
index 8c9efd60..00000000
--- a/matlab_version/experiments_in_the_wild/Collect_wild_imgs.m
+++ /dev/null
@@ -1,226 +0,0 @@
-function [images, detections, labels] = Collect_wild_imgs(root_test_data)
-       
-    use_afw = true;
-    use_lfpw = true;
-    use_helen = true;
-    use_ibug = true;
-    
-    use_68 = true;           
-    
-    images = [];
-    labels = [];
-    detections = [];
-    
-    if(use_afw)
-        [img, det, lbl] = Collect_AFW(root_test_data, use_68);
-        images = cat(1, images, img');
-        detections = cat(1, detections, det);
-        labels = cat(1, labels, lbl);        
-    end
-    
-    if(use_lfpw)
-        [img, det, lbl] = Collect_LFPW(root_test_data, use_68);
-        images = cat(1, images, img');
-        detections = cat(1, detections, det);
-        labels = cat(1, labels, lbl);        
-    end    
-    
-    if(use_ibug)
-        [img, det, lbl] = Collect_ibug(root_test_data, use_68);
-        images = cat(1, images, img');
-        detections = cat(1, detections, det);
-        labels = cat(1, labels, lbl);        
-    end
-    
-    if(use_helen)
-        [img, det, lbl] = Collect_helen(root_test_data, use_68);
-        images = cat(1, images, img');
-        detections = cat(1, detections, det);
-        labels = cat(1, labels, lbl);        
-    end    
-
-    % convert to format expected by the Fitting method
-    detections(:,3) = detections(:,1) + detections(:,3);
-    detections(:,4) = detections(:,2) + detections(:,4);
-    
-
-end
-
-
-function [images, detections, labels] = Collect_AFW(root_test_data, use_68)
-    
-dataset_loc = [root_test_data, '/AFW/'];
-
-landmarkLabels = dir([dataset_loc '\*.pts']);
-
-num_imgs = size(landmarkLabels,1);
-
-images = struct;
-if(use_68)
-    labels = zeros(num_imgs, 68, 2);    
-else
-    labels = zeros(num_imgs, 66, 2);
-end
-
-detections = zeros(num_imgs, 4);
-
-load([root_test_data, '/Bounding Boxes/bounding_boxes_afw.mat']);
-num_landmarks = 68;
-
-for imgs = 1:num_imgs
-
-    [~,name,~] = fileparts(landmarkLabels(imgs).name);
-        
-    landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
-    
-    if(~use_68)
-        inds_frontal = [1:60,62:64,66:68];
-        landmarks = landmarks(inds_frontal,:);
-    end
-    
-    images(imgs).img = [dataset_loc,  name '.jpg'];
-    labels(imgs,:,:) = landmarks;
-                   
-    detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
-    
-end
-
-detections(:,3) = detections(:,3) - detections(:,1);
-detections(:,4) = detections(:,4) - detections(:,2);
-
-end
-
-function [images, detections, labels] = Collect_LFPW(root_test_data, use_68)
-    
-dataset_loc = [root_test_data, '/lfpw/testset/'];
-
-landmarkLabels = dir([dataset_loc '\*.pts']);
-
-num_imgs = size(landmarkLabels,1);
-
-images = struct;
-if(use_68)
-    labels = zeros(num_imgs, 68, 2);    
-else
-    labels = zeros(num_imgs, 66, 2);
-end
-
-detections = zeros(num_imgs, 4);
-
-load([root_test_data, '/Bounding Boxes/bounding_boxes_lfpw_testset.mat']);
-
-num_landmarks = 68;
-
-for imgs = 1:num_imgs
-
-    [~,name,~] = fileparts(landmarkLabels(imgs).name);
-        
-    landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
-
-    if(~use_68)
-        inds_frontal = [1:60,62:64,66:68];
-        landmarks = landmarks(inds_frontal,:);
-    end
-    
-    images(imgs).img = [dataset_loc,  name '.png'];
-    
-    labels(imgs,:,:) = landmarks;
-    
-    detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
-    
-end
-
-detections(:,3) = detections(:,3) - detections(:,1);
-detections(:,4) = detections(:,4) - detections(:,2);
-
-
-end
-
-function [images, detections, labels] = Collect_ibug(root_test_data, use_68)
-    
-dataset_loc = [root_test_data, '/ibug/'];
-
-landmarkLabels = dir([dataset_loc '\*.pts']);
-
-num_imgs = size(landmarkLabels,1);
-
-images = struct;
-
-if(use_68)
-    labels = zeros(num_imgs, 68, 2);    
-else
-    labels = zeros(num_imgs, 66, 2);
-end
-
-detections = zeros(num_imgs, 4);
-
-load([root_test_data, '/Bounding Boxes/bounding_boxes_ibug.mat']);
-num_landmarks = 68;
-
-for imgs = 1:num_imgs
-
-    [~,name,~] = fileparts(landmarkLabels(imgs).name);
-        
-    landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
-
-    if(~use_68)
-        inds_frontal = [1:60,62:64,66:68];
-        landmarks = landmarks(inds_frontal,:);
-    end
-    
-    images(imgs).img = [dataset_loc,  name '.jpg'];
-    
-    labels(imgs,:,:) = landmarks;
-    
-    detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
-    
-end
-
-detections(:,3) = detections(:,3) - detections(:,1);
-detections(:,4) = detections(:,4) - detections(:,2);
-
-end
-
-function [images, detections, labels] = Collect_helen(root_test_data, use_68)
-    
-dataset_loc = [root_test_data, '/helen/testset/'];
-
-landmarkLabels = dir([dataset_loc '\*.pts']);
-
-num_imgs = size(landmarkLabels,1);
-
-images = struct;
-
-if(use_68)
-    labels = zeros(num_imgs, 68, 2);    
-else
-    labels = zeros(num_imgs, 66, 2);
-end
-
-detections = zeros(num_imgs, 4);
-
-load([root_test_data, '/Bounding Boxes/bounding_boxes_helen_testset.mat']);
-num_landmarks = 68;
-for imgs = 1:num_imgs
-
-    [~,name,~] = fileparts(landmarkLabels(imgs).name);
-    
-    landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
-    
-    if(~use_68)
-        inds_frontal = [1:60,62:64,66:68];
-        landmarks = landmarks(inds_frontal,:);
-    end
-    
-    images(imgs).img = [dataset_loc,  name '.jpg'];
-    
-    labels(imgs,:,:) = landmarks;
-    
-    detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
-    
-end
-
-detections(:,3) = detections(:,3) - detections(:,1);
-detections(:,4) = detections(:,4) - detections(:,2);
-
-end
diff --git a/matlab_version/experiments_in_the_wild/Collect_wild_imgs_train.m b/matlab_version/experiments_in_the_wild/Collect_wild_imgs_train.m
deleted file mode 100644
index de04310b..00000000
--- a/matlab_version/experiments_in_the_wild/Collect_wild_imgs_train.m
+++ /dev/null
@@ -1,121 +0,0 @@
-function [images, detections, labels] = Collect_wild_imgs_train(root_test_data)
-       
-    use_lfpw = true;
-    use_helen = true;
-    
-    use_68 = true;           
-    
-    images = [];
-    labels = [];
-    detections = [];
-        
-    if(use_lfpw)
-        [img, det, lbl] = Collect_LFPW(root_test_data, use_68);
-        images = cat(1, images, img');
-        detections = cat(1, detections, det);
-        labels = cat(1, labels, lbl);        
-    end    
-        
-    if(use_helen)
-        [img, det, lbl] = Collect_helen(root_test_data, use_68);
-        images = cat(1, images, img');
-        detections = cat(1, detections, det);
-        labels = cat(1, labels, lbl);        
-    end    
-
-    % convert to format expected by the Fitting method
-    detections(:,3) = detections(:,1) + detections(:,3);
-    detections(:,4) = detections(:,2) + detections(:,4);
-    
-
-end
-
-function [images, detections, labels] = Collect_LFPW(root_test_data, use_68)
-    
-dataset_loc = [root_test_data, '/lfpw/trainset/'];
-
-landmarkLabels = dir([dataset_loc '\*.pts']);
-
-num_imgs = size(landmarkLabels,1);
-
-images = struct;
-if(use_68)
-    labels = zeros(num_imgs, 68, 2);    
-else
-    labels = zeros(num_imgs, 66, 2);
-end
-
-detections = zeros(num_imgs, 4);
-
-load([root_test_data, '/Bounding Boxes/bounding_boxes_lfpw_trainset.mat']);
-
-num_landmarks = 68;
-
-for imgs = 1:num_imgs
-
-    [~,name,~] = fileparts(landmarkLabels(imgs).name);
-        
-    landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
-
-    if(~use_68)
-        inds_frontal = [1:60,62:64,66:68];
-        landmarks = landmarks(inds_frontal,:);
-    end
-    
-    images(imgs).img = [dataset_loc,  name '.png'];
-    
-    labels(imgs,:,:) = landmarks;
-    
-    detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
-    
-end
-
-detections(:,3) = detections(:,3) - detections(:,1);
-detections(:,4) = detections(:,4) - detections(:,2);
-
-
-end
-
-function [images, detections, labels] = Collect_helen(root_test_data, use_68)
-    
-dataset_loc = [root_test_data, '/helen/trainset/'];
-
-landmarkLabels = dir([dataset_loc '\*.pts']);
-
-num_imgs = size(landmarkLabels,1);
-
-images = struct;
-
-if(use_68)
-    labels = zeros(num_imgs, 68, 2);    
-else
-    labels = zeros(num_imgs, 66, 2);
-end
-
-detections = zeros(num_imgs, 4);
-
-load([root_test_data, '/Bounding Boxes/bounding_boxes_helen_trainset.mat']);
-num_landmarks = 68;
-for imgs = 1:num_imgs
-
-    [~,name,~] = fileparts(landmarkLabels(imgs).name);
-    
-    landmarks = dlmread([dataset_loc, landmarkLabels(imgs).name], ' ', [3,0,num_landmarks+2,1]);
-    
-    if(~use_68)
-        inds_frontal = [1:60,62:64,66:68];
-        landmarks = landmarks(inds_frontal,:);
-    end
-    
-    images(imgs).img = [dataset_loc,  name '.jpg'];
-    
-    labels(imgs,:,:) = landmarks;
-    
-    detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
-    
-end
-
-detections(:,3) = detections(:,3) - detections(:,1);
-detections(:,4) = detections(:,4) - detections(:,2);
-
-end
diff --git a/matlab_version/experiments_in_the_wild/Display_clnf_dclm_results.m b/matlab_version/experiments_in_the_wild/Display_clnf_dclm_results.m
deleted file mode 100644
index 8a5406fc..00000000
--- a/matlab_version/experiments_in_the_wild/Display_clnf_dclm_results.m
+++ /dev/null
@@ -1,63 +0,0 @@
-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_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,:,:) - 0.5;
-shapes = shapes(18: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/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,:,:) - 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', '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');
-ylabel('Proportion of images','FontName','Helvetica');
-grid on
-title('Fitting on 300W','FontSize',60,'FontName','Helvetica');
-
-legend('show', 'Location', 'SouthEast');
-print -dpng results/dclm_results.png
diff --git a/matlab_version/experiments_in_the_wild/Display_clnf_dclm_results_sparse.m b/matlab_version/experiments_in_the_wild/Display_clnf_dclm_results_sparse.m
deleted file mode 100644
index 21f0f868..00000000
--- a/matlab_version/experiments_in_the_wild/Display_clnf_dclm_results_sparse.m
+++ /dev/null
@@ -1,49 +0,0 @@
-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,:,:) - 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', 'DCLM', 'LineWidth',line_width);
-
-load('results/results_wild_dclm_general_sparse_h.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', 'DCLM sparse', 'LineWidth',line_width);
-
-set(gca,'xtick',[0:0.01:0.08])
-xlim([0,0.08]);
-xlabel('Size normalised shape RMS error','FontName','Helvetica');
-ylabel('Proportion of images','FontName','Helvetica');
-grid on
-title('Fitting on 300W','FontSize',60,'FontName','Helvetica');
-
-legend('show', 'Location', 'SouthEast');
-% print -dpng results/dclm_results.png
diff --git a/matlab_version/experiments_in_the_wild/Display_clnf_results_general.m b/matlab_version/experiments_in_the_wild/Display_clnf_results_general.m
deleted file mode 100644
index fb41ee9c..00000000
--- a/matlab_version/experiments_in_the_wild/Display_clnf_results_general.m
+++ /dev/null
@@ -1,116 +0,0 @@
-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_clnf_general.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,:,:);
-
-clnf_error = compute_error( labels,  shapes);
-
-[error_x, error_y] = cummErrorCurve(clnf_error);
-hold on;
-
-plot(error_x, error_y, 'g','DisplayName', 'CLNF', 'LineWidth',line_width);
-
-load('results/results_wild_clm_general.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, '--b','DisplayName', 'CLM+', 'LineWidth',line_width);
-
-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,:,:) - 0.5;
-shapes = shapes(18:end,:,:);
-
-clnf_hierarch_error = compute_error( labels,  shapes);
-
-[error_x, error_y] = cummErrorCurve(clnf_hierarch_error);
-
-plot(error_x, error_y, 'r','DisplayName', 'OpenFace', 'LineWidth',line_width);
-
-
-set(gca,'xtick',[0:0.02:0.06])
-xlim([0,0.08]);
-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');
-
-%% 
-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','Times New Roman');
-
-line_width = 6;
-hold on;
-
-load('results/results_wild_clnf_general.mat');
-labels = experiments.labels - 0.5;
-shapes = experiments.shapes;
-
-clnf_error = compute_error( labels,  shapes);
-
-[error_x, error_y] = cummErrorCurve(clnf_error);
-hold on;
-
-plot(error_x, error_y, '--g','DisplayName', 'CLNF', 'LineWidth',line_width);
-
-load('results/results_wild_clm_general.mat');
-experiments(1).labels = experiments(1).labels - 0.5;
-experiments(1).shapes = experiments(1).shapes;
-
-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_clnf_general_final_inner.mat');
-experiments(1).labels = experiments(1).labels - 0.5;
-experiments(1).shapes = experiments(1).shapes;
-
-clnf_hierarch_error = compute_error( experiments(1).labels,  experiments(1).shapes);
-
-[error_x, error_y] = cummErrorCurve(clnf_hierarch_error);
-
-plot(error_x, error_y, 'r','DisplayName', 'CLNF hierarch', 'LineWidth',line_width);
-
-set(gca,'xtick',[0:0.02:0.08])
-xlim([0,0.08]);
-xlabel('Size normalised shape RMS error','FontName','Times New Roman');
-ylabel('Proportion of images','FontName','Times New Roman');
-grid on
-title('Fitting in the wild with outline','FontSize',60,'FontName','Times New Roman');
-
-legend('show', 'Location', 'SouthEast');
diff --git a/matlab_version/experiments_in_the_wild/Errors_PDM_basic.mat b/matlab_version/experiments_in_the_wild/Errors_PDM_basic.mat
deleted file mode 100644
index f83dba9e..00000000
Binary files a/matlab_version/experiments_in_the_wild/Errors_PDM_basic.mat and /dev/null differ
diff --git a/matlab_version/experiments_in_the_wild/Errors_PDM_eyes.mat b/matlab_version/experiments_in_the_wild/Errors_PDM_eyes.mat
deleted file mode 100644
index 53f3aa1e..00000000
Binary files a/matlab_version/experiments_in_the_wild/Errors_PDM_eyes.mat and /dev/null differ
diff --git a/matlab_version/experiments_in_the_wild/Script_CLM_general.m b/matlab_version/experiments_in_the_wild/Script_CLM_general.m
deleted file mode 100644
index df9ebf1f..00000000
--- a/matlab_version/experiments_in_the_wild/Script_CLM_general.m
+++ /dev/null
@@ -1,185 +0,0 @@
-function Script_CLM_general()
-
-addpath('../PDM_helpers/');
-addpath('../fitting/normxcorr2_mex_ALL');
-addpath('../fitting/');
-addpath('../models/');
-
-% Replace this with the location of in 300 faces in the wild data
-if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
-    root_test_data = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];    
-else
-    root_test_data = 'F:/Dropbox/Dropbox/AAM/test data/';
-end
-
-[images, detections, labels] = Collect_wild_imgs(root_test_data);
-
-%% loading the patch experts
-   
-clmParams = struct;
-
-clmParams.window_size = [25,25; 23,23; 21,21;];
-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
-
-verbose = false; % set to true to visualise the fitting
-output_root = './wild_fit_clm_general/';
-
-% 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);
- 
-% the default model parameters to use
-clmParams.regFactor = [35, 27, 20, 5];
-clmParams.sigmaMeanShift = [1.25, 1.375, 1.5, 1.75]; 
-clmParams.tikhonov_factor = [2.5, 5, 7.5, 12.5];
-
-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;
-   
-% 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;
-
-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;
-
-    % shape correction for matlab format
-    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.lhoods = lhoods;
-experiment.shapes = shapes_all;
-experiment.labels = labels_all;
-experiment.errors_normed = compute_error(labels_all - 0.5, shapes_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_clm_general.mat';
-save(output_results, 'experiments');
-    
-end
diff --git a/matlab_version/experiments_in_the_wild/Script_CLM_general_vis_resp.m b/matlab_version/experiments_in_the_wild/Script_CLM_general_vis_resp.m
deleted file mode 100644
index 66d6b983..00000000
--- a/matlab_version/experiments_in_the_wild/Script_CLM_general_vis_resp.m
+++ /dev/null
@@ -1,82 +0,0 @@
-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
diff --git a/matlab_version/experiments_in_the_wild/Script_CLNF_general.m b/matlab_version/experiments_in_the_wild/Script_CLNF_general.m
deleted file mode 100644
index cddb448e..00000000
--- a/matlab_version/experiments_in_the_wild/Script_CLNF_general.m
+++ /dev/null
@@ -1,187 +0,0 @@
-function Script_CLNF_general()
-
-addpath('../PDM_helpers/');
-addpath('../fitting/normxcorr2_mex_ALL');
-addpath('../fitting/');
-addpath('../CCNF/');
-addpath('../models/');
-
-% Replace this with the location of in 300 faces in the wild data
-if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
-    root_test_data = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];    
-else
-    root_test_data = '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;];
-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
-patches = patches(1);
-verbose = false; % set to true to visualise the fitting
-output_root = './wild_fit_clnf/';
-
-% 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];
-clmParams.sigmaMeanShift = [1.25, 1.375, 1.5]; 
-clmParams.tikhonov_factor = [2.5, 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 = 3;
-
-% 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;
-
-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_clnf_general.mat';
-save(output_results, 'experiments');
-    
-end
diff --git a/matlab_version/experiments_in_the_wild/Script_CLNF_general_vis_resp.m b/matlab_version/experiments_in_the_wild/Script_CLNF_general_vis_resp.m
deleted file mode 100644
index 73843056..00000000
--- a/matlab_version/experiments_in_the_wild/Script_CLNF_general_vis_resp.m
+++ /dev/null
@@ -1,82 +0,0 @@
-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
diff --git a/matlab_version/experiments_in_the_wild/Script_DCLM_general.m b/matlab_version/experiments_in_the_wild/Script_DCLM_general.m
deleted file mode 100644
index 584597e9..00000000
--- a/matlab_version/experiments_in_the_wild/Script_DCLM_general.m
+++ /dev/null
@@ -1,186 +0,0 @@
-function Script_DCLM_general()
-
-addpath('../PDM_helpers/');
-addpath('../fitting/normxcorr2_mex_ALL');
-addpath('../fitting/');
-addpath('../CCNF/');
-addpath('../models/');
-
-% Replace this with the location of in 300 faces in the wild data
-if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
-    root_test_data = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];    
-else
-    root_test_data = '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/dpn/', 'dpn_patches_*_general.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 = 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, 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_general.mat';
-save(output_results, 'experiments');
-    
-end
diff --git a/matlab_version/experiments_in_the_wild/Script_DCLM_general_sparse.m b/matlab_version/experiments_in_the_wild/Script_DCLM_general_sparse.m
deleted file mode 100644
index 06f38cfe..00000000
--- a/matlab_version/experiments_in_the_wild/Script_DCLM_general_sparse.m
+++ /dev/null
@@ -1,211 +0,0 @@
-function Script_DCLM_general_sparse()
-
-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/dpn/', 'dpn_patches_*_general.mat', [], [], clmParams);
-
-%% Fitting the model to the provided image
-
-output_root = './wild_fit_dclm_sparse/';
-
-% 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 = false;
-tic
-
-
-% for recording purposes
-experiment.params = clmParams;
-
-% Turn off the visibilities for scale 0, as we do not need to detect all
-% landmarks in the first scale
-to_rem_from = [1,2,3,6,7];
-% Median based
-%to_rem_init = [68;56;4;34;29;39;19;62;59;18;54;14;11;61;30;42;48;41;52;51;44;33;9;63;2;16;58;25;28;67];%;23;35;26;7;];
-% Mean based
-
-to_rem_1 = [4;68;58;62;51;6;59;20;63;53;25;56;14;64;9;67;2;33;11;37;17;52;26;60;28;34;44;38;29;8;21;15;12;18];
-to_rem_2 = [6;62;50;25;59;20;17;66;64;57;39;14;12;68;41;45;34;43;30;60;4;29;1;61;47;9;65;52;37;22;15;35;54;58];
-to_rem_3 = [66;62;54;60;38;5;30;13;28;59;44];
-
-patches(1).visibilities(to_rem_from, to_rem_1) = 0;
-patches(2).visibilities(to_rem_from, to_rem_2) = 0;
-patches(3).visibilities(to_rem_from, to_rem_3) = 0;
-
-% If the third scale the same as first, does it work?
-% patches(3).visibilities(to_rem_from, to_rem_3) = 0;
-
-% patches(4).visibilities(to_rem_from, to_rem_2) = 0;
-
-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_general_sparse_h.mat';
-save(output_results, 'experiments');
-    
-end
diff --git a/matlab_version/experiments_in_the_wild/Script_DCLM_general_vis_resp.m b/matlab_version/experiments_in_the_wild/Script_DCLM_general_vis_resp.m
deleted file mode 100644
index 3f072154..00000000
--- a/matlab_version/experiments_in_the_wild/Script_DCLM_general_vis_resp.m
+++ /dev/null
@@ -1,82 +0,0 @@
-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
diff --git a/matlab_version/experiments_in_the_wild/Script_DCLM_general_vis_select.m b/matlab_version/experiments_in_the_wild/Script_DCLM_general_vis_select.m
deleted file mode 100644
index 5f95421e..00000000
--- a/matlab_version/experiments_in_the_wild/Script_DCLM_general_vis_select.m
+++ /dev/null
@@ -1,232 +0,0 @@
-function Script_DCLM_general_vis_select()
-
-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 = 'C:\Users\tbaltrus\Documents\DATA/300-W/';
-end
-
-[images, detections, labels] = Collect_wild_imgs_train(root_test_data);
-
-% Only use a subset of images as otherwise it will take a bit too long
-rng(0);
-subset = randperm(numel(images), 750);
-images = images(subset);
-detections = detections(subset,:);
-labels = labels(subset,:,:);
-
-%% loading the patch experts
-   
-clmParams = struct;
-
-clmParams.window_size = [25,25; 23,23; 21,21;];
-clmParams.numPatchIters = size(clmParams.window_size,1);
-
-[patches] = Load_DCLM_Patch_Experts( '../models/dpn/', 'dpn_patches_*_general.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 = 3;
-
-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 = false;
-tic
-
-experiments_full = struct;
-
-best_so_far = [66;62;54;60;38;5;30;13;28;59;44];
-
-% The first patch removal
-to_rem_1 = [4;68;58;62;51;6;59;20;63;53;25;56;14;64;9;67;2;33;11;37;17;52;26;60;28;34;44;38;29;8;21;15;12;18];
-to_rem_2 = [6;62;50;25;59;20;17;66;64;57;39;14;12;68;41;45;34;43;30;60;4;29;1;61;47;9;65;52;37;22;15;35;54;58];
-to_rem_from = [1,2,3,6,7];
-% patches(1).visibilities(to_rem_from,to_rem_init) = 0;
-
-% A greedy method for removing visibilities
-for to_rem = 1:23
-
-    % Not ideal ones to turn off, but a much faster version
-    all_ids = 1:68;
-    visibility_off = setdiff(all_ids, best_so_far);
-    inds = randperm(numel(visibility_off), 20);
-    visibility_off = visibility_off(inds);
-    
-    % for recording purposes
-    experiment.params = clmParams;
-
-    for w=visibility_off
-
-        patches(1).visibilities(to_rem_from,to_rem_1) = 0;
-        patches(2).visibilities(to_rem_from,to_rem_2) = 0;
-        patches(3).visibilities(to_rem_from,:) = 1;
-        to_rem_c = cat(1, best_so_far, w);
-        if(w > 0)
-            patches(3).visibilities(to_rem_from,to_rem_c) = 0;
-        end
-        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.v_off = w;
-        experiment.err = mean(experiment.errors_normed);
-        experiment.err_med = median(experiment.errors_normed);
-        % 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));
-
-    end
-    
-    % Grab the best suceeding switch off
-    [~,id] = min(cat(1, experiments.err));
-    best_so_far = cat(1, best_so_far, experiments(id).v_off);
-    experiments_full(numel(best_so_far)).best_so_far = best_so_far;
-    experiments_full(numel(best_so_far)).error_mean = experiments(id).err;
-    experiments_full(numel(best_so_far)).error_med = experiments(id).err_med;
-    output_results = 'results/results_wild_dclm_vis_off_scale3.mat';
-    save(output_results, 'experiments_full'); 
-    clear experiments
-    clear experiment
-end
-%%
-
-    
-end
diff --git a/matlab_version/experiments_in_the_wild/Script_DCLM_general_vis_select_base.m b/matlab_version/experiments_in_the_wild/Script_DCLM_general_vis_select_base.m
deleted file mode 100644
index 4a94c6ca..00000000
--- a/matlab_version/experiments_in_the_wild/Script_DCLM_general_vis_select_base.m
+++ /dev/null
@@ -1,233 +0,0 @@
- function Script_DCLM_general_vis_select_base()
-
-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_train(root_test_data);
-
-% Only use a subset of images as otherwise it will take a bit too long
-rng(0);
-subset = randperm(numel(images), 500);
-images = images(subset);
-detections = detections(subset,:);
-labels = labels(subset,:,:);
-
-%% 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/dpn/', 'dpn_patches_*_general.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;
-
-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 = false;
-tic
-
-experiments_full = struct;
-
-best_so_far = [];
-
-% Base is 0.033 mean and 0.0291 median
-
-% The first patch removal
-% to_rem_init = [68;56;4;34;29;39;19;62;59;18;54;14;11;61;30;42;48;41;52;51;44;33;9;63;2;16;58;25;28;67;23;35;26;7;];
-% to_rem_from = [1,2,3,6,7];
-% patches(1).visibilities(to_rem_from,to_rem_init) = 0;
-
-% A greedy method for removing visibilities
-for to_rem = 1:1
-
-    visibility_off = 0:1:68;
-    visibility_off = setdiff(visibility_off, best_so_far);
-    
-    % for recording purposes
-    experiment.params = clmParams;
-
-    for w=visibility_off
-
-
-%         patches(2).visibilities(to_rem_from,:) = 1;
-        to_rem_c = cat(1, best_so_far, w);
-        if(w > 0)
-            patches(2).visibilities(to_rem_from,to_rem_c) = 0;
-        end
-        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;
-        experiment.visibilities = patches(1).visibilities;
-        experiment.v_off = w;
-        experiment.err = median(experiment.errors_normed);
-        % 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));
-
-    end
-    
-    % Grab the best suceeding switch off
-    [~,id] = min(cat(1, experiments.err));
-    best_so_far = cat(1, best_so_far, experiments(id).v_off);
-    experiments_full(numel(best_so_far)).best_so_far = best_so_far;
-    experiments_full(numel(best_so_far)).error = experiments(id).err;
-    output_results = 'results/results_wild_dclm_vis_on.mat';
-    save(output_results, 'experiments_full'); 
-    clear experiments
-    clear experiment
-end
-%%
-
-    
-end
diff --git a/matlab_version/experiments_in_the_wild/Script_DCLM_general_vis_select_scale_0.m b/matlab_version/experiments_in_the_wild/Script_DCLM_general_vis_select_scale_0.m
deleted file mode 100644
index 580f013a..00000000
--- a/matlab_version/experiments_in_the_wild/Script_DCLM_general_vis_select_scale_0.m
+++ /dev/null
@@ -1,199 +0,0 @@
-function Script_DCLM_general_vis_select_scale_0()
-
-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_train(root_test_data);
-
-% Only use a subset of images as otherwise it will take a bit too long
-rng(0);
-subset = randperm(numel(images), 1000);
-images = images(subset);
-detections = detections(subset,:);
-labels = labels(subset,:,:);
-
-%% loading the patch experts
-   
-clmParams = struct;
-
-clmParams.window_size = [25,25; 23,23; 21,21;];
-clmParams.numPatchIters = size(clmParams.window_size,1);
-
-[patches] = Load_DCLM_Patch_Experts( '../models/dpn/', 'dpn_patches_*_general.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 = 1;
-
-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 = false;
-tic
-
-experiments_full = struct;
-
-% The first patch removal
-to_rem_from = [1,2,3,6,7];
-
-
-% A greedy method for removing visibilities
-for reps = 1:500
-
-    % Removing half of the landmarks
-    to_rem = randperm(68, 34);
-        
-    patches(1).visibilities(to_rem_from,:) = 1;
-
-    patches(1).visibilities(to_rem_from, to_rem) = 0;
-    
-    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
-
-    experiments_full(reps).to_rem = to_rem;
-    experiments_full(reps).mean_error = mean(compute_error(labels_all - 0.5, shapes_all));
-    experiments_full(reps).median_error = median(compute_error(labels_all - 0.5, shapes_all));
-
-    output_results = 'results/results_wild_dclm_vis_off_scale_0.mat';
-    save(output_results, 'experiments_full'); 
-
-end
-%%
-
-    
-end
diff --git a/matlab_version/experiments_in_the_wild/Script_DCLM_general_wild.m b/matlab_version/experiments_in_the_wild/Script_DCLM_general_wild.m
deleted file mode 100644
index 3071f31d..00000000
--- a/matlab_version/experiments_in_the_wild/Script_DCLM_general_wild.m
+++ /dev/null
@@ -1,186 +0,0 @@
-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
diff --git a/matlab_version/experiments_in_the_wild/compute_error.m b/matlab_version/experiments_in_the_wild/compute_error.m
deleted file mode 100644
index 8debb0f8..00000000
--- a/matlab_version/experiments_in_the_wild/compute_error.m
+++ /dev/null
@@ -1,41 +0,0 @@
-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
diff --git a/matlab_version/experiments_in_the_wild/cummErrorCurve.m b/matlab_version/experiments_in_the_wild/cummErrorCurve.m
deleted file mode 100644
index e18a1ada..00000000
--- a/matlab_version/experiments_in_the_wild/cummErrorCurve.m
+++ /dev/null
@@ -1,19 +0,0 @@
-function [x, y] = cummErrorCurve( errorVec )
-%CUMMERRORCURVE Summary of this function goes here
-%   Detailed explanation goes here
-
-    
-    spacing = 0.001;       
-    
-    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
-end
-
diff --git a/matlab_version/experiments_in_the_wild/hierarch_checks/Display_clnf_results_hierarch.m b/matlab_version/experiments_in_the_wild/hierarch_checks/Display_clnf_results_hierarch.m
deleted file mode 100644
index dd35f903..00000000
--- a/matlab_version/experiments_in_the_wild/hierarch_checks/Display_clnf_results_hierarch.m
+++ /dev/null
@@ -1,202 +0,0 @@
-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_clnf_general_final_inner.mat');
-labels = experiments.labels - 0.5;
-shapes = experiments.shapes;
-labels(1:17,:,:) = [];
-shapes(1:17,:,:) = [];
-
-[clnf_error, err_pp] = compute_error( labels,  shapes);
-
-[error_x, error_y] = cummErrorCurve(clnf_error);
-hold on;
-
-plot(error_x, error_y, 'b','DisplayName', 'CLNF inner', 'LineWidth',line_width);
-
-load('results/results_wild_clnf_general.mat');
-labels = experiments.labels - 0.5;
-shapes = experiments.shapes;
-labels(1:17,:,:) = [];
-shapes(1:17,:,:) = [];
-
-[clnf_error, err_pp] = compute_error( labels,  shapes);
-
-[error_x, error_y] = cummErrorCurve(clnf_error);
-hold on;
-
-plot(error_x, error_y, 'g','DisplayName', 'CLNF', 'LineWidth',line_width);
-
-load('results/results_wild_clnf_general_hierarch.mat');
-labels = experiments.labels - 0.5;
-shapes = experiments.shapes;
-
-labels(1:17,:,:) = [];
-shapes(1:17,:,:) = [];
-[clnf_error, err_pp_h] = compute_error( labels,  shapes);
-
-[error_x, error_y] = cummErrorCurve(clnf_error);
-hold on;
-
-plot(error_x, error_y, 'r','DisplayName', 'CLNF hierarch', 'LineWidth',line_width);
-
-set(gca,'xtick',[0:0.025:0.1])
-xlim([0,0.1]);
-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');
-
-%% 
-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_general_final_inner.mat');
-labels = experiments.labels - 0.5;
-shapes = experiments.shapes;
-
-clnf_error_left = compute_error_point_to_line_left_eye( labels,  shapes);
-clnf_error_right = compute_error_point_to_line_right_eye( labels,  shapes);
-clnf_error = cat(1, clnf_error_left, clnf_error_right);
-
-[error_x, error_y] = cummErrorCurve(clnf_error);
-hold on;
-
-plot(error_x, error_y, 'b','DisplayName', 'CLNF', 'LineWidth',line_width);
-
-load('results/results_wild_clnf_general_hierarch.mat');
-labels = experiments.labels - 0.5;
-shapes = experiments.shapes;
-
-clnf_error_left = compute_error_point_to_line_left_eye( labels,  shapes);
-clnf_error_right = compute_error_point_to_line_right_eye( labels,  shapes);
-clnf_error = cat(1, clnf_error_left, clnf_error_right);
-
-[error_x, error_y] = cummErrorCurve(clnf_error);
-hold on;
-
-plot(error_x, error_y, 'r','DisplayName', 'CLNF hierarch', 'LineWidth',line_width);
-
-set(gca,'xtick',[0:0.01:0.05])
-xlim([0,0.05]);
-xlabel('Size normalised eyelid 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');
-
-%% 
-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_general_final_inner.mat');
-labels = experiments.labels - 0.5;
-shapes = experiments.shapes;
-
-clnf_error = compute_mouth_error( labels,  shapes);
-
-[error_x, error_y] = cummErrorCurve(clnf_error);
-hold on;
-
-plot(error_x, error_y, 'b','DisplayName', 'CLNF', 'LineWidth',line_width);
-
-load('results/results_wild_clnf_general_hierarch.mat');
-labels = experiments.labels - 0.5;
-shapes = experiments.shapes;
-
-clnf_error = compute_mouth_error( labels,  shapes);
-
-[error_x, error_y] = cummErrorCurve(clnf_error);
-hold on;
-
-plot(error_x, error_y, 'r','DisplayName', 'CLNF hierarch', 'LineWidth',line_width);
-
-set(gca,'xtick',[0:0.02:0.08])
-xlim([0,0.08]);
-xlabel('Size normalised mouth 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');
-
-%% 
-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_general_final_inner.mat');
-labels = experiments.labels - 0.5;
-shapes = experiments.shapes;
-
-clnf_error = compute_brow_error_to_line( labels,  shapes);
-
-[error_x, error_y] = cummErrorCurve(clnf_error);
-hold on;
-
-plot(error_x, error_y, 'b','DisplayName', 'CLNF', 'LineWidth',line_width);
-
-load('results/results_wild_clnf_general_hierarch.mat');
-labels = experiments.labels - 0.5;
-shapes = experiments.shapes;
-
-clnf_error = compute_brow_error_to_line( labels,  shapes);
-
-[error_x, error_y] = cummErrorCurve(clnf_error);
-hold on;
-
-plot(error_x, error_y, 'r','DisplayName', 'CLNF hierarch', 'LineWidth',line_width);
-
-set(gca,'xtick',[0:0.02:0.08])
-xlim([0,0.08]);
-xlabel('Brow error','FontName','Helvetica');
-ylabel('Proportion of images','FontName','Helvetica');
-grid on
-title('Fitting in the wild','FontSize',60,'FontName','Helvetica');
-
-legend('show', 'Location', 'SouthEast');
\ No newline at end of file
diff --git a/matlab_version/experiments_in_the_wild/hierarch_checks/Errors_PDM_basic.mat b/matlab_version/experiments_in_the_wild/hierarch_checks/Errors_PDM_basic.mat
deleted file mode 100644
index 2111488a..00000000
Binary files a/matlab_version/experiments_in_the_wild/hierarch_checks/Errors_PDM_basic.mat and /dev/null differ
diff --git a/matlab_version/experiments_in_the_wild/hierarch_checks/Errors_PDM_eyes.mat b/matlab_version/experiments_in_the_wild/hierarch_checks/Errors_PDM_eyes.mat
deleted file mode 100644
index 783c4ffa..00000000
Binary files a/matlab_version/experiments_in_the_wild/hierarch_checks/Errors_PDM_eyes.mat and /dev/null differ
diff --git a/matlab_version/experiments_in_the_wild/hierarch_checks/Script_CLNF_general_eyes.m b/matlab_version/experiments_in_the_wild/hierarch_checks/Script_CLNF_general_eyes.m
deleted file mode 100644
index 4e86e78d..00000000
--- a/matlab_version/experiments_in_the_wild/hierarch_checks/Script_CLNF_general_eyes.m
+++ /dev/null
@@ -1,196 +0,0 @@
-function Script_CLNF_general_eyes()
-
-addpath('../PDM_helpers/');
-addpath('../fitting/normxcorr2_mex_ALL');
-addpath('../fitting/');
-addpath('../CCNF/');
-addpath('../models/');
-
-% Replace this with the location of in 300 faces in the wild data
-if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
-    root_test_data = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];    
-else
-    root_test_data = 'F:/Dropbox/Dropbox/AAM/test data/';
-end
-
-[images, detections, labels] = Collect_wild_imgs(root_test_data);
-
-%% loading the patch experts
-   
-clmParams = struct;
-
-clmParams.window_size = [25,25; 23,23; 21,21;];
-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
-
-verbose = false; % set to true to visualise the fitting
-output_root = './wild_fit_clnf/';
-
-% the default PDM to use
-pdmLoc = ['../models/pdm/pdm_68_aligned_wild_eyes.mat'];
-
-load(pdmLoc);
-
-pdm = struct;
-pdm.M = double(M);
-pdm.E = double(E);
-pdm.V = double(V);
-
-% the default model parameters to use
-clmParams.regFactor = 25;               
-clmParams.sigmaMeanShift = 2;
-clmParams.tikhonov_factor = 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;
-   
-% for recording purposes
-experiment.params = clmParams;
-
-num_points = numel(M)/3;
-
-errors = zeros(numel(images),1);
-shapes_all = zeros(size(labels,2),size(labels,3), size(labels,1));
-labels_all = zeros(size(labels,2),size(labels,3), size(labels,1));
-errors_normed = zeros(numel(images),1);
-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;
-
-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;
-
-    % TODO the shape correction?
-    shape = shape + 1;
-
-    shapes_all(:,:,i) = shape;
-    labels_all(:,:,i) = labels(i,:,:);
-
-    if(mod(i, 200)==0)
-        fprintf('%d done\n', i );
-    end
-
-    valid_points =  sum(squeeze(labels(i,:,:)),2) > 0;
-    valid_points(1:17) = 0;
-
-    actualShape = squeeze(labels(i,:,:));
-    errors(i) = sqrt(mean(sum((actualShape(valid_points,:) - shape(valid_points,:)).^2,2)));      
-    width = ((max(actualShape(valid_points,1)) - min(actualShape(valid_points,1)))+(max(actualShape(valid_points,2)) - min(actualShape(valid_points,2))))/2;
-    errors_normed(i) = errors(i)/width;                                    
-    lhoods(i) = lhood;
-    if(verbose)
-        [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 = errors;
-experiment.errors_normed = errors_normed;
-experiment.lhoods = lhoods;
-experiment.shapes = shapes_all;
-experiment.labels = labels_all;
-experiment.ibug_error = compute_error(labels_all, shapes_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(errors_normed), median(errors_normed));
-
-%%
-output_results = 'results/results_wild_clnf_general_eyes.mat';
-save(output_results, 'experiments');
-    
-end
diff --git a/matlab_version/experiments_in_the_wild/hierarch_checks/Script_CLNF_general_hierarch.m b/matlab_version/experiments_in_the_wild/hierarch_checks/Script_CLNF_general_hierarch.m
deleted file mode 100644
index 8b3258f5..00000000
--- a/matlab_version/experiments_in_the_wild/hierarch_checks/Script_CLNF_general_hierarch.m
+++ /dev/null
@@ -1,252 +0,0 @@
-function Script_CLNF_general_hierarch()
-
-addpath('../PDM_helpers/');
-addpath('../fitting/normxcorr2_mex_ALL');
-addpath('../fitting/');
-addpath('../CCNF/');
-addpath('../models/');
-
-% Replace this with the location of in 300 faces in the wild data
-if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
-    root_test_data = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];    
-else
-    root_test_data = 'F:/Dropbox/Dropbox/AAM/test data/';
-end
-
-[images, detections, labels] = Collect_wild_imgs(root_test_data);
-
-%% loading the patch experts and PDMs
-   
-clmParams = struct;
-
-clmParams.window_size = [25,25; 23,23; 21,21;];
-clmParams.numPatchIters = size(clmParams.window_size,1);
-
-[patches] = Load_Patch_Experts( '../models/general/', 'ccnf_patches_*_general.mat', [], [], clmParams);
-
-verbose = true; % set to true to visualise the fitting
-output_root = './wild_fit_clnf_hierarch/';
-
-% 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);
-
-% the default full face model parameters to use
-clmParams.regFactor = [35, 27, 20];
-clmParams.sigmaMeanShift = [1.25, 1.375, 1.5]; 
-clmParams.tikhonov_factor = [2.5, 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;
-
-% Loading eye PDM and patch experts
-[clmParams_eye, pdm_right_eye, pdm_left_eye] = Load_CLM_params_eye();
-[patches_right_eye] = Load_Patch_Experts( '../models/hierarch/', 'ccnf_patches_*_combined.mat', [], [], clmParams_eye);
-[patches_left_eye] = Load_Patch_Experts( '../models/hierarch/', 'left_ccnf_patches_*_combined.mat', [], [], clmParams_eye);
-clmParams_eye.multi_modal_types  = patches_right_eye(1).multi_modal_types;
-right_eye_inds = [43,44,45,46,47,48];
-left_eye_inds = [37,38,39,40,41,42];
-
-% Loading mouth PDM and patch experts
-[clmParams_mouth, pdm_mouth] = Load_CLM_params_mouth();
-[patches_mouth] = Load_Patch_Experts( '../models/hierarch/', 'ccnf_patches_*_mouth_mv.mat', [], [], clmParams_mouth);
-clmParams_mouth.multi_modal_types  = patches_mouth(1).multi_modal_types;
-mouth_inds = 49:68;
-
-% Loading brow PDM and patch experts
-[clmParams_brow, pdm_brow] = Load_CLM_params_brows();
-[patches_brow] = Load_Patch_Experts( '../models/hierarch/', 'ccnf_patches_*_brow.mat', [], [], clmParams_brow);
-clmParams_mouth.multi_modal_types  = patches_brow(1).multi_modal_types;
-brow_inds = 18:27;
-
-%% 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;
-
-%% Fitting the model to the provided images
-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;
-
-    %% now fit the hierarchical models (eyes and mouth)
-                            
-    % Perform eye fitting now
-    shape_r_eye = shape(right_eye_inds, :);
-
-    [ a, R, T, ~, l_params] = fit_PDM_ortho_proj_to_2D_no_reg(pdm_right_eye.M, pdm_right_eye.E, pdm_right_eye.V, shape_r_eye);
-
-    bbox = [min(shape_r_eye(:,1)), min(shape_r_eye(:,2)), max(shape_r_eye(:,1)), max(shape_r_eye(:,2))];
-
-    g_param = [a; Rot2Euler(R)'; T];
-
-    [shape_r_eye] = Fitting_from_bb(image, [], bbox, pdm_right_eye, patches_right_eye, clmParams_eye, 'gparam', g_param, 'lparam', l_params);
-
-    % Perform eye fitting now 
-    shape_l_eye = shape(left_eye_inds, :);
-
-    [ a, R, T, ~, l_params] = fit_PDM_ortho_proj_to_2D_no_reg(pdm_left_eye.M, pdm_left_eye.E, pdm_left_eye.V, shape_l_eye);
-
-    bbox = [min(shape_l_eye(:,1)), min(shape_l_eye(:,2)), max(shape_l_eye(:,1)), max(shape_l_eye(:,2))];
-
-    g_param = [a; Rot2Euler(R)'; T];
-
-    [shape_l_eye] = Fitting_from_bb(image, [], bbox, pdm_left_eye, patches_left_eye, clmParams_eye, 'gparam', g_param, 'lparam', l_params);
-
-    % Perform mouth fitting now 
-    shape_mouth = shape(mouth_inds, :);
-
-    [ a, R, T, ~, l_params] = fit_PDM_ortho_proj_to_2D_no_reg(pdm_mouth.M, pdm_mouth.E, pdm_mouth.V, shape_mouth);
-
-    bbox = [min(shape_mouth(:,1)), min(shape_mouth(:,2)), max(shape_mouth(:,1)), max(shape_mouth(:,2))];
-
-    g_param = [a; Rot2Euler(R)'; T];
-
-    [shape_mouth] = Fitting_from_bb(image, [], bbox, pdm_mouth, patches_mouth, clmParams_mouth, 'gparam', g_param, 'lparam', l_params);
-
-    % Perform brow fitting now 
-    shape_brow = shape(brow_inds, :);
-
-    [ a, R, T, ~, l_params] = fit_PDM_ortho_proj_to_2D_no_reg(pdm_brow.M, pdm_brow.E, pdm_brow.V, shape_brow);
-    g_param = [a; Rot2Euler(R)'; T];
-
-    bbox = [min(shape_brow(:,1)), min(shape_brow(:,2)), max(shape_brow(:,1)), max(shape_brow(:,2))];
-
-    [shape_brow] = Fitting_from_bb(image, [], bbox, pdm_brow, patches_brow, clmParams_brow, 'gparam', g_param, 'lparam', l_params);
-    
-    % Now after detections incorporate the eyes back
-    % into the face model
-
-    shape(left_eye_inds, :) = shape_l_eye;
-    shape(right_eye_inds, :) = shape_r_eye;
-    shape(mouth_inds, :) = shape_mouth;
-    shape(brow_inds, :) = shape_brow;
-    
-    [ ~, ~, ~, ~, ~, ~, shape_fit] = fit_PDM_ortho_proj_to_2D_no_reg(pdm.M, pdm.E, pdm.V, shape);
-    
-    %% Incorporate the hierarchical models back into the joint PDM
-    
-    shapes_all(:,:,i) = shape_fit;
-    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.lhoods = lhoods;
-experiment.shapes = shapes_all;
-experiment.labels = labels_all;
-experiment.errors_normed = compute_error(labels_all - 0.5, shapes_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_clnf_general_hierarch.mat';
-save(output_results, 'experiments');
-    
-end
diff --git a/matlab_version/experiments_in_the_wild/hierarch_checks/Script_PDM_eyes.m b/matlab_version/experiments_in_the_wild/hierarch_checks/Script_PDM_eyes.m
deleted file mode 100644
index e83a29d6..00000000
--- a/matlab_version/experiments_in_the_wild/hierarch_checks/Script_PDM_eyes.m
+++ /dev/null
@@ -1,80 +0,0 @@
-function Script_PDM_eyes()
-
-addpath('../PDM_helpers/');
-addpath('../fitting/normxcorr2_mex_ALL');
-addpath('../fitting/');
-addpath('../CCNF/');
-addpath('../models/');
-
-% Replace this with the location of in 300 faces in the wild data
-if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
-    root_test_data = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];    
-else
-    root_test_data = 'F:/Dropbox/Dropbox/AAM/test data/';
-end
-
-[images, detections, labels] = Collect_wild_imgs(root_test_data);
-
-%% Fitting the model to the provided image
-
-% the default PDM to use
-pdmLoc = ['../models/pdm/pdm_68_aligned_wild_eyes.mat'];
-
-load(pdmLoc);
-
-pdm = struct;
-pdm.M = double(M);
-pdm.E = double(E);
-pdm.V = double(V);
-
-num_points = numel(M)/3;
-
-errors = zeros(numel(images),1);
-shapes_all = zeros(size(labels,2),size(labels,3), size(labels,1));
-labels_all = zeros(size(labels,2),size(labels,3), size(labels,1));
-errors_normed = zeros(numel(images),1);
-
-errors_left_eye = zeros(numel(images),1);
-errors_right_eye = zeros(numel(images),1);
-
-tic
-for i=1:numel(images)
-
-    image = imread(images(i).img);
-    image_orig = image;
-    
-    if(size(image,3) == 3)
-        image = rgb2gray(image);
-    end              
-    
-    labels_curr = squeeze(labels(i,:,:));
-    
-    [ a, R, T, ~, l_params, err, shapeOrtho] = fit_PDM_ortho_proj_to_2D(pdm.M, pdm.E, pdm.V, labels_curr);
-
-    shape = shapeOrtho;
-    shapes_all(:,:,i) = shapeOrtho;
-    labels_all(:,:,i) = labels_curr;
-
-    if(mod(i, 200)==0)
-        fprintf('%d done\n', i );
-    end
-
-    valid_points =  sum(squeeze(labels(i,:,:)),2) > 0;
-    valid_points(1:17) = 0;
-
-    actualShape = squeeze(labels(i,:,:));
-    errors(i) = sqrt(mean(sum((actualShape(valid_points,:) - shape(valid_points,:)).^2,2)));      
-    width = ((max(actualShape(valid_points,1)) - min(actualShape(valid_points,1)))+(max(actualShape(valid_points,2)) - min(actualShape(valid_points,2))))/2;
-    errors_normed(i) = errors(i)/width;     
-
-    errors_left_eye(i) = compute_error_point_to_line_left_eye(actualShape, shapeOrtho, [0]);
-    errors_right_eye(i) = compute_error_point_to_line_right_eye(actualShape, shapeOrtho, [0]);
-
-    if(errors_normed(i) > 0.035 || errors_left_eye(i) > 0.035 || errors_right_eye(i) > 0.035)
-        imshow(image);hold on; plot(shape(:,1), shape(:,2), '.g'); hold off;
-    end
-end
-  
-save('Errors_PDM_eyes.mat', 'errors_normed', 'errors_left_eye', 'errors_right_eye');
-
-end
diff --git a/matlab_version/experiments_in_the_wild/hierarch_checks/Script_PDM_general.m b/matlab_version/experiments_in_the_wild/hierarch_checks/Script_PDM_general.m
deleted file mode 100644
index fb52e2ed..00000000
--- a/matlab_version/experiments_in_the_wild/hierarch_checks/Script_PDM_general.m
+++ /dev/null
@@ -1,80 +0,0 @@
-function Script_PDM_general()
-
-addpath('../PDM_helpers/');
-addpath('../fitting/normxcorr2_mex_ALL');
-addpath('../fitting/');
-addpath('../CCNF/');
-addpath('../models/');
-
-% Replace this with the location of in 300 faces in the wild data
-if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
-    root_test_data = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];    
-else
-    root_test_data = 'F:/Dropbox/Dropbox/AAM/test data/';
-end
-
-[images, detections, labels] = Collect_wild_imgs(root_test_data);
-
-%% Fitting the model to the provided image
-
-% the default PDM to use
-pdmLoc = ['../models/pdm/pdm_68_aligned_wild.mat'];
-
-load(pdmLoc);
-
-pdm = struct;
-pdm.M = double(M);
-pdm.E = double(E);
-pdm.V = double(V);
-
-num_points = numel(M)/3;
-
-errors = zeros(numel(images),1);
-shapes_all = zeros(size(labels,2),size(labels,3), size(labels,1));
-labels_all = zeros(size(labels,2),size(labels,3), size(labels,1));
-errors_normed = zeros(numel(images),1);
-
-errors_left_eye = zeros(numel(images),1);
-errors_right_eye = zeros(numel(images),1);
-
-tic
-for i=1:numel(images)
-
-    image = imread(images(i).img);
-    image_orig = image;
-    
-    if(size(image,3) == 3)
-        image = rgb2gray(image);
-    end              
-    
-    labels_curr = squeeze(labels(i,:,:));
-    
-    [ a, R, T, ~, l_params, err, shapeOrtho] = fit_PDM_ortho_proj_to_2D(pdm.M, pdm.E, pdm.V, labels_curr);
-
-    shape = shapeOrtho;
-    shapes_all(:,:,i) = shapeOrtho;
-    labels_all(:,:,i) = labels_curr;
-
-    if(mod(i, 200)==0)
-        fprintf('%d done\n', i );
-    end
-
-    valid_points =  sum(squeeze(labels(i,:,:)),2) > 0;
-    valid_points(1:17) = 0;
-
-    actualShape = squeeze(labels(i,:,:));
-    errors(i) = sqrt(mean(sum((actualShape(valid_points,:) - shape(valid_points,:)).^2,2)));      
-    width = ((max(actualShape(valid_points,1)) - min(actualShape(valid_points,1)))+(max(actualShape(valid_points,2)) - min(actualShape(valid_points,2))))/2;
-    errors_normed(i) = errors(i)/width;     
-
-    errors_left_eye(i) = compute_error_point_to_line_left_eye(actualShape, shapeOrtho, [0]);
-    errors_right_eye(i) = compute_error_point_to_line_right_eye(actualShape, shapeOrtho, [0]);
-
-    if(errors_normed(i) > 0.035 || errors_left_eye(i) > 0.035 || errors_right_eye(i) > 0.035)
-        imshow(image);hold on; plot(shape(:,1), shape(:,2), '.g'); hold off;
-    end
-end
-  
-save('Errors_PDM_basic.mat', 'errors_normed', 'errors_left_eye', 'errors_right_eye');
-
-end
diff --git a/matlab_version/experiments_in_the_wild/hierarch_checks/compute_brow_error_to_line.m b/matlab_version/experiments_in_the_wild/hierarch_checks/compute_brow_error_to_line.m
deleted file mode 100644
index 594a8ba0..00000000
--- a/matlab_version/experiments_in_the_wild/hierarch_checks/compute_brow_error_to_line.m
+++ /dev/null
@@ -1,89 +0,0 @@
-function [ error_per_image ] = compute_brow_error( ground_truth_all, detected_points_all, occluded )
-%compute_error
-%   compute the average point-to-point Euclidean error of right eye 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
-
-brow_inds_from_68 = 18:27;
-brow_inds_from_49 = 1:10;
-
-brow_inds_from_66 = 18:27;
-
-num_of_images = size(ground_truth_all,3);
-
-num_points_gt = size(ground_truth_all,1);
-
-num_points_det = size(detected_points_all,1);
-
-error_per_image = zeros(num_of_images,1);
-
-for i =1:num_of_images
-    
-    if(num_points_det == 10)
-        detected_points = detected_points_all(:,:,i);
-    elseif(num_points_det == 68)
-        detected_points = detected_points_all(brow_inds_from_68,:,i);           
-    elseif(num_points_det == 66)
-        detected_points = detected_points_all(brow_inds_from_66,:,i);           
-    elseif(num_points_det == 49)
-        detected_points = detected_points_all(brow_inds_from_49,:,i);        
-    end
-    
-    ground_truth_points  = ground_truth_all(:,:,i);
-    
-    if(num_points_gt == 66 || num_points_gt == 68)
-        interocular_distance = norm(ground_truth_points(37,:)-ground_truth_points(46,:));
-        ground_truth_points = ground_truth_points(brow_inds_from_68,:,:);
-    else
-        interocular_distance = norm(ground_truth_points(37-17,:)-ground_truth_points(46-17,:));
-        ground_truth_points = ground_truth_points(brow_inds_from_68,:,:);
-    end
-    
-    sum=0;
-    
-    for j=1:size(detected_points,1)
-        
-        if(j== 1 || j == 5 || j == 6 || j == 10)
-        % eye corners should align perfectly
-            sum = sum + norm(detected_points(j,:)-ground_truth_points(j,:));
-        else
-            sum = sum + point_to_segments(detected_points(j,:), ground_truth_points(j-1:j+1,:));
-        end
-    end
-    
-    error_per_image(i) = sum/(size(detected_points,1)*interocular_distance);
-end
-
-if(nargin > 2)
-    error_per_image = error_per_image(~occluded);
-end
-
-end
-
-function seg_dist = point_to_segments(point, segments)
-    
-    seg_dists = zeros(size(segments, 1)-1,1);
-
-    for i=1:size(segments, 1)-1
-       
-        vec1 = point - segments(i,:);
-        vec2 = segments(i+1,:) - segments(i,:);
-        
-        d = (vec1 * vec2') / (norm(vec2)^2);
-        
-        if(d < 0)
-            seg_dists(i) = norm(vec1);
-        elseif(d > 1)
-            seg_dists(i) = norm(point - segments(i+1,:));
-        else
-            seg_dists(i) = sqrt( norm(vec1)^2 - norm(d * vec2)^2);
-        end
-    end
-    seg_dist = min(seg_dists);
-end
\ No newline at end of file
diff --git a/matlab_version/experiments_in_the_wild/hierarch_checks/compute_error_point_to_line_left_eye.m b/matlab_version/experiments_in_the_wild/hierarch_checks/compute_error_point_to_line_left_eye.m
deleted file mode 100644
index 4b93b432..00000000
--- a/matlab_version/experiments_in_the_wild/hierarch_checks/compute_error_point_to_line_left_eye.m
+++ /dev/null
@@ -1,86 +0,0 @@
-function [ error_per_image ] = compute_error_point_to_line_left_eye( ground_truth_all, detected_points_all, occluded )
-%compute_error
-%   compute the average point-to-point Euclidean error normalized by the
-%   inter-ocular distance (measured as the Euclidean distance between the
-%   outer corners of the eyes)
-%
-%   Inputs:
-%          grounth_truth_all, size: num_of_points x 2 x num_of_images
-%          detected_points_all, size: num_of_points x 2 x num_of_images
-%   Output:
-%          error_per_image, size: num_of_images x 1
-
-
-
-right_eye_inds_from_68 = [37,38,39,40,41,42,37];
-right_eye_inds_from_28 = [9,11,13,15,17,19];
-
-num_of_images = size(ground_truth_all,3);
-
-num_points_gt = size(ground_truth_all,1);
-
-num_points_det = size(detected_points_all,1);
-
-error_per_image = zeros(num_of_images,1);
-
-for i =1:num_of_images
-    
-    if(num_points_det == 6)
-        detected_points = detected_points_all(:,:,i);
-    elseif(num_points_det == 68 || num_points_det == 66)
-        detected_points = detected_points_all(right_eye_inds_from_68,:,i);        
-    elseif(num_points_det == 28)
-        detected_points = detected_points_all(right_eye_inds_from_28,:,i);        
-    elseif(num_points_det == 49)
-        
-    end
-    ground_truth_points  = ground_truth_all(:,:,i);
-    
-    if(num_points_gt == 66 || num_points_gt == 68)
-        interocular_distance = norm(ground_truth_points(37,:)-ground_truth_points(46,:));
-        ground_truth_points = ground_truth_points(right_eye_inds_from_68,:,:);
-    else
-        interocular_distance = norm(ground_truth_points(37-17,:)-ground_truth_points(46-17,:));
-        ground_truth_points = ground_truth_points(right_eye_inds_from_68,:,:);
-    end
-    
-    sum=0;
-    for j=1:6
-        
-        if(j== 1 || j == 6)
-            % eye corners should align perfectly
-            sum = sum + norm(detected_points(j,:)-ground_truth_points(j,:));
-        else
-            % points between eye corners measured in distance to the two appropriate line
-            % segments
-            sum = sum + point_to_segments(detected_points(j,:), ground_truth_points(j-1:j+1,:));
-        end
-    end
-    error_per_image(i) = sum/(6*interocular_distance);
-end
-
-error_per_image = error_per_image(~occluded);
-
-end
-
-function seg_dist = point_to_segments(point, segments)
-    
-    seg_dists = zeros(size(segments, 1)-1,1);
-
-    for i=1:size(segments, 1)-1
-       
-        vec1 = point - segments(i,:);
-        vec2 = segments(i+1,:) - segments(i,:);
-        
-        d = (vec1 * vec2') / (norm(vec2)^2);
-        
-        if(d < 0)
-            seg_dists(i) = norm(vec1);
-        elseif(d > 1)
-            seg_dists(i) = norm(point - segments(i+1,:));
-        else
-            seg_dists(i) = sqrt( norm(vec1)^2 - norm(d * vec2)^2);
-        end
-    end
-    seg_dist = min(seg_dists);
-end
\ No newline at end of file
diff --git a/matlab_version/experiments_in_the_wild/hierarch_checks/compute_error_point_to_line_right_eye.m b/matlab_version/experiments_in_the_wild/hierarch_checks/compute_error_point_to_line_right_eye.m
deleted file mode 100644
index b6dd94a0..00000000
--- a/matlab_version/experiments_in_the_wild/hierarch_checks/compute_error_point_to_line_right_eye.m
+++ /dev/null
@@ -1,86 +0,0 @@
-function [ error_per_image ] = compute_error_point_to_line_right_eye( ground_truth_all, detected_points_all, occluded )
-%compute_error
-%   compute the average point-to-point Euclidean error normalized by the
-%   inter-ocular distance (measured as the Euclidean distance between the
-%   outer corners of the eyes)
-%
-%   Inputs:
-%          grounth_truth_all, size: num_of_points x 2 x num_of_images
-%          detected_points_all, size: num_of_points x 2 x num_of_images
-%   Output:
-%          error_per_image, size: num_of_images x 1
-
-
-
-right_eye_inds_from_68 = [43,44,45,46,47,48,43];
-right_eye_inds_from_28 = [9,11,13,15,17,19];
-
-num_of_images = size(ground_truth_all,3);
-
-num_points_gt = size(ground_truth_all,1);
-
-num_points_det = size(detected_points_all,1);
-
-error_per_image = zeros(num_of_images,1);
-
-for i =1:num_of_images
-    
-    if(num_points_det == 6)
-        detected_points = detected_points_all(:,:,i);
-    elseif(num_points_det == 68 || num_points_det == 66)
-        detected_points = detected_points_all(right_eye_inds_from_68,:,i);        
-    elseif(num_points_det == 28)
-        detected_points = detected_points_all(right_eye_inds_from_28,:,i);        
-    elseif(num_points_det == 49)
-        
-    end
-    ground_truth_points  = ground_truth_all(:,:,i);
-    
-    if(num_points_gt == 66 || num_points_gt == 68)
-        interocular_distance = norm(ground_truth_points(37,:)-ground_truth_points(46,:));
-        ground_truth_points = ground_truth_points(right_eye_inds_from_68,:,:);
-    else
-        interocular_distance = norm(ground_truth_points(37-17,:)-ground_truth_points(46-17,:));
-        ground_truth_points = ground_truth_points(right_eye_inds_from_68,:,:);
-    end
-    
-    sum=0;
-    for j=1:6
-        
-        if(j== 1 || j == 6)
-            % eye corners should align perfectly
-            sum = sum + norm(detected_points(j,:)-ground_truth_points(j,:));
-        else
-            % points between eye corners measured in distance to the two appropriate line
-            % segments
-            sum = sum + point_to_segments(detected_points(j,:), ground_truth_points(j-1:j+1,:));
-        end
-    end
-    error_per_image(i) = sum/(6*interocular_distance);
-end
-
-error_per_image = error_per_image(~occluded);
-
-end
-
-function seg_dist = point_to_segments(point, segments)
-    
-    seg_dists = zeros(size(segments, 1)-1,1);
-
-    for i=1:size(segments, 1)-1
-       
-        vec1 = point - segments(i,:);
-        vec2 = segments(i+1,:) - segments(i,:);
-        
-        d = (vec1 * vec2') / (norm(vec2)^2);
-        
-        if(d < 0)
-            seg_dists(i) = norm(vec1);
-        elseif(d > 1)
-            seg_dists(i) = norm(point - segments(i+1,:));
-        else
-            seg_dists(i) = sqrt( norm(vec1)^2 - norm(d * vec2)^2);
-        end
-    end
-    seg_dist = min(seg_dists);
-end
\ No newline at end of file
diff --git a/matlab_version/experiments_in_the_wild/hierarch_checks/compute_mouth_error.m b/matlab_version/experiments_in_the_wild/hierarch_checks/compute_mouth_error.m
deleted file mode 100644
index 8c79c7ba..00000000
--- a/matlab_version/experiments_in_the_wild/hierarch_checks/compute_mouth_error.m
+++ /dev/null
@@ -1,66 +0,0 @@
-function [ error_per_image ] = compute_mouth_error( ground_truth_all, detected_points_all, occluded )
-%compute_error
-%   compute the average point-to-point Euclidean error of right eye 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
-
-mouth_inds_from_68 = 49:68;
-mouth_inds_from_49 = 30:49;
-
-mouth_inds_from_66 = 49:66;
-
-mouth_inds_from_20 = [1:12, 14:16, 18:20];
-
-num_of_images = size(ground_truth_all,3);
-
-num_points_gt = size(ground_truth_all,1);
-
-num_points_det = size(detected_points_all,1);
-
-error_per_image = zeros(num_of_images,1);
-
-for i =1:num_of_images
-    
-    if(num_points_det == 20)
-        detected_points = detected_points_all(:,:,i);
-    elseif(num_points_det == 68)
-        detected_points = detected_points_all(mouth_inds_from_68,:,i);           
-    elseif(num_points_det == 66)
-        detected_points = detected_points_all(mouth_inds_from_66,:,i);           
-    elseif(num_points_det == 49)
-        detected_points = detected_points_all(mouth_inds_from_49,:,i);        
-    end
-    
-    ground_truth_points  = ground_truth_all(:,:,i);
-    
-    if(num_points_gt == 66 || num_points_gt == 68)
-        interocular_distance = norm(ground_truth_points(37,:)-ground_truth_points(46,:));
-        ground_truth_points = ground_truth_points(mouth_inds_from_68,:,:);
-    else
-        interocular_distance = norm(ground_truth_points(37-17,:)-ground_truth_points(46-17,:));
-        ground_truth_points = ground_truth_points(mouth_inds_from_68,:,:);
-    end
-    
-    if(size(detected_points,1) == 18 && size(ground_truth_points,1) == 20)
-        ground_truth_points = ground_truth_points(mouth_inds_from_20,:);
-    end
-    sum=0;
-    
-    for j=1:size(detected_points,1)
-        sum = sum+norm(detected_points(j,:)-ground_truth_points(j,:));
-    end
-    
-    error_per_image(i) = sum/(size(detected_points,1)*interocular_distance);
-end
-
-if(nargin > 2)
-    error_per_image = error_per_image(~occluded);
-end
-
-end
diff --git a/matlab_version/experiments_in_the_wild/hierarch_checks/plotcov2.m b/matlab_version/experiments_in_the_wild/hierarch_checks/plotcov2.m
deleted file mode 100644
index bbd223b2..00000000
--- a/matlab_version/experiments_in_the_wild/hierarch_checks/plotcov2.m
+++ /dev/null
@@ -1,182 +0,0 @@
-% PLOTCOV2 - Plots a covariance ellipse with major and minor axes
-%            for a bivariate Gaussian distribution.
-%
-% Usage:
-%   h = plotcov2(mu, Sigma[, OPTIONS]);
-% 
-% Inputs:
-%   mu    - a 2 x 1 vector giving the mean of the distribution.
-%   Sigma - a 2 x 2 symmetric positive semi-definite matrix giving
-%           the covariance of the distribution (or the zero matrix).
-%
-% Options:
-%   'conf'    - a scalar between 0 and 1 giving the confidence
-%               interval (i.e., the fraction of probability mass to
-%               be enclosed by the ellipse); default is 0.9.
-%   'num-pts' - the number of points to be used to plot the
-%               ellipse; default is 100.
-%
-% This function also accepts options for PLOT.
-%
-% Outputs:
-%   h     - a vector of figure handles to the ellipse boundary and
-%           its major and minor axes
-%
-% See also: PLOTCOV3
-
-% Copyright (C) 2002 Mark A. Paskin
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-function h = plotcov2(mu, Sigma, varargin)
-
-if size(Sigma) ~= [2 2], error('Sigma must be a 2 by 2 matrix'); end
-if length(mu) ~= 2, error('mu must be a 2 by 1 vector'); end
-
-[p, ...
- n, ...
- plot_opts] = process_options(varargin, 'conf', 0.9, ...
-					'num-pts', 100);
-h = [];
-holding = ishold;
-if (Sigma == zeros(2, 2))
-  z = mu;
-else
-  % Compute the Mahalanobis radius of the ellipsoid that encloses
-  % the desired probability mass.
-  k = conf2mahal(p, 2);
-  % The major and minor axes of the covariance ellipse are given by
-  % the eigenvectors of the covariance matrix.  Their lengths (for
-  % the ellipse with unit Mahalanobis radius) are given by the
-  % square roots of the corresponding eigenvalues.
-  if (issparse(Sigma))
-    [V, D] = eigs(Sigma);
-  else
-    [V, D] = eig(Sigma);
-  end
-  % Compute the points on the surface of the ellipse.
-  t = linspace(0, 2*pi, n);
-  u = [cos(t); sin(t)];
-  w = (k * V * sqrt(D)) * u;
-  z = repmat(mu, [1 n]) + w;
-  % Plot the major and minor axes.
-  L = k * sqrt(diag(D));
-  h = plot([mu(1); mu(1) + L(1) * V(1, 1)], ...
-	   [mu(2); mu(2) + L(1) * V(2, 1)], plot_opts{:});
-  hold on;
-  h = [h; plot([mu(1); mu(1) + L(2) * V(1, 2)], ...
-	       [mu(2); mu(2) + L(2) * V(2, 2)], plot_opts{:})];
-end
-
-h = [h; plot(z(1, :), z(2, :), plot_opts{:})];
-if (~holding) hold off; end
-end
-
-function [varargout] = process_options(args, varargin)
-
-% Check the number of input arguments
-n = length(varargin);
-if (mod(n, 2))
-  error('Each option must be a string/value pair.');
-end
-
-% Check the number of supplied output arguments
-if (nargout < (n / 2))
-  error('Insufficient number of output arguments given');
-elseif (nargout == (n / 2))
-  warn = 1;
-  nout = n / 2;
-else
-  warn = 0;
-  nout = n / 2 + 1;
-end
-
-% Set outputs to be defaults
-varargout = cell(1, nout);
-for i=2:2:n
-  varargout{i/2} = varargin{i};
-end
-
-% Now process all arguments
-nunused = 0;
-for i=1:2:length(args)
-  found = 0;
-  for j=1:2:n
-    if strcmpi(args{i}, varargin{j})
-      varargout{(j + 1)/2} = args{i + 1};
-      found = 1;
-      break;
-    end
-  end
-  if (~found)
-    if (warn)
-      warning(sprintf('Option ''%s'' not used.', args{i}));
-      args{i}
-    else
-      nunused = nunused + 1;
-      unused{2 * nunused - 1} = args{i};
-      unused{2 * nunused} = args{i + 1};
-    end
-  end
-end
-
-% Assign the unused arguments
-if (~warn)
-  if (nunused)
-    varargout{nout} = unused;
-  else
-    varargout{nout} = cell(0);
-  end
-end
-end
-
-% CONF2MAHAL - Translates a confidence interval to a Mahalanobis
-%              distance.  Consider a multivariate Gaussian
-%              distribution of the form
-%
-%   p(x) = 1/sqrt((2 * pi)^d * det(C)) * exp((-1/2) * MD(x, m, inv(C)))
-%
-%              where MD(x, m, P) is the Mahalanobis distance from x
-%              to m under P:
-%
-%                 MD(x, m, P) = (x - m) * P * (x - m)'
-%
-%              A particular Mahalanobis distance k identifies an
-%              ellipsoid centered at the mean of the distribution.
-%              The confidence interval associated with this ellipsoid
-%              is the probability mass enclosed by it.  Similarly,
-%              a particular confidence interval uniquely determines
-%              an ellipsoid with a fixed Mahalanobis distance.
-%
-%              If X is an d dimensional Gaussian-distributed vector,
-%              then the Mahalanobis distance of X is distributed
-%              according to the Chi-squared distribution with d
-%              degrees of freedom.  Thus, the Mahalanobis distance is
-%              determined by evaluating the inverse cumulative
-%              distribution function of the chi squared distribution
-%              up to the confidence value.
-%
-% Usage:
-% 
-%   m = conf2mahal(c, d);
-%
-% Inputs:
-%
-%   c    - the confidence interval
-%   d    - the number of dimensions of the Gaussian distribution
-%
-% Outputs:
-%
-%   m    - the Mahalanobis radius of the ellipsoid enclosing the
-%          fraction c of the distribution's probability mass
-%
-% See also: MAHAL2CONF
-
-% Copyright (C) 2002 Mark A. Paskin
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-function m = conf2mahal(c, d)
-
-m = chi2inv(c, d);
-% pr = 0.95 ; c = (1 - pr)/2 ; 
-% m = norminv([c 1-c],0,1) ;
-
-end
\ No newline at end of file
diff --git a/matlab_version/experiments_in_the_wild/hierarch_checks/visualise_errors.m b/matlab_version/experiments_in_the_wild/hierarch_checks/visualise_errors.m
deleted file mode 100644
index 2f9635bd..00000000
--- a/matlab_version/experiments_in_the_wild/hierarch_checks/visualise_errors.m
+++ /dev/null
@@ -1,93 +0,0 @@
-% Visualising the errors
-
-%% First draw the initialisation errors
-if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
-    root_test_data = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];    
-else
-    root_test_data = 'F:/Dropbox/Dropbox/AAM/test data/';
-end
-
-[images, detections, labels] = Collect_wild_imgs(root_test_data);
-
-pdmLoc = ['../models/pdm/pdm_68_aligned_wild.mat'];
-labels = labels - 0.5;
-num_points = size(labels,2);
-
-load(pdmLoc);
-pdm = struct; pdm.M = double(M); pdm.E = double(E); pdm.V = double(V);
-
-shapes_init_all = zeros(num_points, 2, size(detections,1));
-errors_init_all = zeros(size(detections,1), 1);
-views_used = zeros(size(detections,1), 1);
-labels_all = zeros(num_points, 2, size(detections,1));
-
-for i=1:size(detections,1)
-    labels_all(:,:,i) = labels(i,:,:);
-    % Find the best orientation
-    views = [0,0,0; 0,-20,0; 0,20,0; 30,0,0];
-    views = views * pi/180;    
-        
-    shapes_init = zeros(num_points, 2, size(views,1));
-    error_init = zeros(size(views,1),1);
-    
-    for v = 1:size(views,1)                                                                                 
-
-        bbox = detections(i,:);  
-        bbox(2) = bbox(2) + 0.025 * (bbox(4) - bbox(2));
-        rot = Euler2Rot(views(v,:));  
-        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 = (((bbox(3) - bbox(1)) / width_model) + ((bbox(4) - bbox(2))/ height_model)) / 2;
-
-        tx = (bbox(3) + bbox(1))/2;
-        ty = (bbox(4) + bbox(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) = views(v,:);
-        local_params = zeros(size(E));
-        [shape2D] = GetShapeOrtho(M, V, local_params, global_params);
-        shapes_init(:,:,v) = shape2D(:,1:2);        
-        error_init(v) = compute_error(squeeze(labels(i,:,:)), shapes_init(:,:,v));
-    end
-    
-    [~,ind] = min(error_init);
-    views_used(i) = ind;
-    shapes_init_all(:,:,i) = shapes_init(:,:,ind);
-    errors_init_all(i) = error_init(ind);
-end
-[~,~,errs_pp] = compute_error(labels_all, shapes_init_all);
-views_used(errors_init_all > 0.5) = -1;
-
-%%
-load('results/results_wild_clnf_general_hierarch.mat');
-[~,~,errs_pp] = compute_error(labels_all, experiments.shapes);
-% for v=1:size(views,1)
-% % Want to scatter them around mean of each landmark
-%     figure;
-%     set(gca,'YDir','reverse');
-%     rot = Euler2Rot(views(v,:));  
-%     rot_m = rot * reshape(M, num_points, 3)';
-% 
-%     plot(rot_m(1,:), +rot_m(2,:), '.r');
-%     hold on;
-%     for p=1:num_points
-%         errors_x = squeeze(errs_pp(views_used==v,p,1)) * 30;
-%         errors_y = squeeze(errs_pp(views_used==v,p,2)) * 30;
-% %         scatter(bsxfun(@plus, rot_m(1,p), errors_x), bsxfun(@plus, -rot_m(2,p), -errors_y));
-%         sig = cov(errors_x, errors_y);
-%         plotcov2([rot_m(1,p) + mean(errors_x); rot_m(2,p) + mean(errors_y)], sig, 'conf', 0.5);
-%     end
-%    
-% %     plot(rot_m(1,:) + errors(:,1)'*65, -rot_m(2,:) - errors(:,2)'*65, '.b');
-%     axis equal;
-%     hold off;
-% end
\ No newline at end of file
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