function Script_CLNF_wild()

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 = 'D:/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;21,21];
clmParams.numPatchIters = size(clmParams.window_size,1);

[patches] = Load_Patch_Experts( '../models/wild/', 'ccnf_patches_*_wild.mat', [], [], clmParams);

% the default PDM to use
pdmLoc = ['../models/pdm/pdm_68_aligned_wild.mat'];

load(pdmLoc);

pdm = struct;
pdm.M = double(M);
pdm.E = double(E);
pdm.V = double(V);

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;
   
% 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_wild/';
    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));
labels_all = zeros(size(labels,2),size(labels,3), size(labels,1));
lhoods = 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 image

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;                                                                                     

        shapes = zeros(num_points, 2, size(views,1));
        ls = zeros(size(views,1),1);

        % Find the best orientation
        for v = 1:size(views,1)
            [shapes(:,:,v),~,~,ls(v)] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams, 'orientation', views(v,:));                                            
        end

        [lhood, v_ind] = max(ls);
        shape = shapes(:,:,v_ind);

    else
        [shape,~,~,lhood] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams);
    end

    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;

fprintf('Done: mean normed error %.3f median normed error %.4f\n', ...
    mean(experiment.errors_normed), median(experiment.errors_normed));

%%
output_results = 'results/results_clnf_wild.mat';
save(output_results, 'experiment');
    
end