sustaining_gazes/matlab_runners/Feature Point Experiments/Run_OF_on_images.m

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function [ err_outline, err_no_outline ] = Run_OF_on_images(output_loc, database_root, varargin)
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dataset_dirs = {};
if(any(strcmp(varargin, 'use_afw')))
afw_loc = [database_root '/AFW/'];
dataset_dirs = cat(1, dataset_dirs, afw_loc);
end
if(any(strcmp(varargin, 'use_lfpw')))
lfpw_loc = [database_root 'lfpw/testset/'];
dataset_dirs = cat(1, dataset_dirs, lfpw_loc);
end
if(any(strcmp(varargin, 'use_ibug')))
ibug_loc = [database_root 'ibug/'];
dataset_dirs = cat(1, dataset_dirs, ibug_loc);
end
if(any(strcmp(varargin, 'use_helen')))
helen_loc = [database_root '/helen/testset/'];
dataset_dirs = cat(1, dataset_dirs, helen_loc);
end
if(any(strcmp(varargin, 'verbose')))
verbose = true;
else
verbose = false;
end
if(isunix)
executable = '"../../build/bin/FaceLandmarkImg"';
else
executable = '"../../x64/Release/FaceLandmarkImg.exe"';
end
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if(any(strcmp(varargin, 'model')))
model = varargin{find(strcmp(varargin, 'model')) + 1};
else
% the default model is the 68 point in the wild one
model = '"model/main_wild.txt"';
end
if(any(strcmp(varargin, 'multi_view')))
multi_view = varargin{find(strcmp(varargin, 'multi_view')) + 1};
else
multi_view = 0;
end
command = sprintf('%s -mloc %s -multi_view %s -2Dfp ', executable, model, num2str(multi_view));
tic
for i=1:numel(dataset_dirs)
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command_c = sprintf('%s -fdir "%s" -bboxdir "%s" -out_dir "%s" -wild ',...
command, dataset_dirs{i}, dataset_dirs{i}, output_loc);
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if(isunix)
unix(command_c, '-echo');
else
dos(command_c);
end
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end
toc
%%
% Extract the error sizes
dirs = {[database_root '/AFW/'];
[database_root '/ibug/'];
[database_root '/helen/testset/'];
[database_root 'lfpw/testset/'];};
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landmark_dets = dir([output_loc '/*.csv']);
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landmark_det_dir = [output_loc '/'];
num_imgs = size(landmark_dets,1);
labels = zeros(68,2,num_imgs);
shapes = zeros(68,2,num_imgs);
curr = 0;
% work out which columns in the csv file are relevant
tab = readtable([landmark_det_dir, landmark_dets(1).name]);
column_names = tab.Properties.VariableNames;
landmark_inds_x = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'x_'));
landmark_inds_y = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'y_'));
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for i=1:numel(dirs)
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gt_labels = dir([dirs{i}, '*.pts']);
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for g=1:numel(gt_labels)
curr = curr+1;
gt_landmarks = dlmread([dirs{i}, gt_labels(g).name], ' ', 'A4..B71');
[~, name, ~] = fileparts(gt_labels(g).name);
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% find the corresponding detection
all_params = dlmread([landmark_det_dir, name, '.csv'], ',', 1, 0);
landmark_det = [all_params(landmark_inds_x); all_params(landmark_inds_y)]';
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labels(:,:,curr) = gt_landmarks;
if(size(landmark_det,1) == 66)
inds_66 = [[1:60],[62:64],[66:68]];
shapes(inds_66,:,curr) = landmark_det;
else
shapes(:,:,curr) = landmark_det;
end
end
end
% Convert to correct format, so as to have same feature points in ground
% truth and detections
if(size(shapes,2) == 66 && size(labels,2) == 68)
inds_66 = [[1:60],[62:64],[66:68]];
labels = labels(inds_66,:,:);
shapes = shapes(inds_66,:,:);
end
% Center the pixel, and convert to OCV format
labels = labels - 1.5;
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err_outline = compute_error(labels, shapes);
labels_no_out = labels(18:end,:,:);
shapes_no_out = shapes(18:end,:,:);
err_no_outline = compute_error(labels_no_out, shapes_no_out);
%%
save([output_loc, 'res.mat'], 'labels', 'shapes', 'err_outline', 'err_no_outline');
end