369 lines
No EOL
13 KiB
Matlab
369 lines
No EOL
13 KiB
Matlab
function Create_data_train()
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load '../models/pdm/pdm_68_aligned_menpo';
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load '../models/tri_68.mat';
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% This script uses the same format used for patch expert training, and
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% expects the data to be there (this can be found in
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% https://github.com/TadasBaltrusaitis/CCNF)
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% Replace with your location of training data
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dataset_loc = 'C:\Users\tbaltrus\Documents\CCNF\patch_experts\data_preparation/prepared_data/';
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addpath('../PDM_helpers/');
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addpath('./paw_helpers/');
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% Collect Menpo, Multi-PIE and 300W data for training the validator
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scale = '0.5';
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prefix_menpo= 'menpo_train_';
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prefix_mpie_300W = 'combined_';
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% Find the available positive training data
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data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix_menpo, scale));
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data_files_c = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix_mpie_300W, scale));
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centres_all = [];
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for i=1:numel(data_files)
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% Load the orientation of the training data
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load([dataset_loc, '/', data_files(i).name], 'centres');
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centres_all = cat(1, centres_all, centres);
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end
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% Construct mirror indices (which views need to be flipped to create other
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% profile training data)
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mirror_inds = zeros(size(centres_all,1), 1);
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for i=1:numel(data_files)
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% mirrored image has inverse yaw
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mirrored_centre = centres_all(i,:);
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mirrored_centre(2) = -mirrored_centre(2);
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% if mirrored version has same orientation, do not need mirroring
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if(~isequal(mirrored_centre, centres_all(i,:)))
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centres_all = cat(1, centres_all, mirrored_centre);
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mirror_inds = cat(1, mirror_inds, i);
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end
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end
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% Replace with your location of training data
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outputLocation = 'D:\Datasets/detection_validation/prep_data/';
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num_more_neg = 10;
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% Make sure same data generated all the time
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rng(0);
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% Negative samples from teh INRIAPerson dataset
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neg_image_loc = 'D:\Datasets\INRIAPerson\INRIAPerson\Train\neg/';
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neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png']));
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max_img_used = 8000;
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% do it separately for centers due to memory limitations
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for r=1:size(centres_all,1)
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a_mod = 0.4;
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mirror = false;
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if(mirror_inds(r) ~= 0 )
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mirror = true;
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label_mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;...
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32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;...
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61,65;62,64;68,66];
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load([dataset_loc, '/', data_files_c(mirror_inds(r)).name]);
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all_images_t = all_images;
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landmark_locations_t = landmark_locations;
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visiIndex_t = visiIndex;
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load([dataset_loc, '/', data_files(mirror_inds(r)).name]);
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% Combining Menpo + MPIE + 300W
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all_images = cat(1, all_images, all_images_t);
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landmark_locations = cat(1, landmark_locations, landmark_locations_t);
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% Taking a subset of visibilities from all the datasets
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visiIndex = visiIndex_t & visiIndex;
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else
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load([dataset_loc, '/', data_files_c(r).name]);
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all_images_t = all_images;
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landmark_locations_t = landmark_locations;
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visiIndex_t = visiIndex;
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load([dataset_loc, '/', data_files(r).name]);
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all_images = cat(1, all_images, all_images_t);
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landmark_locations = cat(1, landmark_locations, landmark_locations_t);
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visiIndex = visiIndex_t & visiIndex;
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end
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visiCurrent = logical(visiIndex);
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if(mirror)
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centres = [centres(1), -centres(2), -centres(3)];
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tmp1 = visiCurrent(label_mirror_inds(:,1));
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tmp2 = visiCurrent(label_mirror_inds(:,2));
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visiCurrent(label_mirror_inds(:,2)) = tmp1;
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visiCurrent(label_mirror_inds(:,1)) = tmp2;
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end
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visibleVerts = 1:numel(visiCurrent);
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visibleVerts = visibleVerts(visiCurrent)-1;
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% Correct the triangulation to take into account the vertex
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% visibilities
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triangulation = [];
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shape = a_mod * Euler2Rot(centres * pi/180) * reshape(M, numel(M)/3, 3)';
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shape = shape';
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for i=1:size(T,1)
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visib = 0;
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for j=1:numel(visibleVerts)
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if(T(i,1)==visibleVerts(j))
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visib = visib+1;
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end
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if(T(i,2)==visibleVerts(j))
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visib = visib+1;
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end
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if(T(i,3)==visibleVerts(j))
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visib = visib+1;
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end
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end
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% Only if all three of the vertices are visible
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if(visib == 3)
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% Also want to remove triangles facing the wrong way (self occluded)
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v1 = [shape(T(i,1)+1,1), shape(T(i,1)+1,2), shape(T(i,1)+1,3)];
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v2 = [shape(T(i,2)+1,1), shape(T(i,2)+1,2), shape(T(i,2)+1,3)];
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v3 = [shape(T(i,3)+1,1), shape(T(i,3)+1,2), shape(T(i,3)+1,3)];
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normal = cross((v2-v1), v3 - v2);
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normal = normal / norm(normal);
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direction = normal * [0,0,1]';
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% And only if the triangle is facing the camera
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if(direction > 0)
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triangulation = cat(1, triangulation, T(i,:));
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end
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end
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end
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% Initialise the warp
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[ alphas, betas, triX, mask, minX, minY, nPix ] = InitialisePieceWiseAffine(triangulation, shape);
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mask = logical(mask);
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imgs_to_use = randperm(size(landmark_locations, 1));
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if(size(landmark_locations, 1) > max_img_used)
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imgs_to_use = imgs_to_use(1:max_img_used);
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end
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% Extracting relevant filenames
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examples = zeros(numel(imgs_to_use) * (num_more_neg+1), nPix);
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errors = zeros(numel(imgs_to_use) * (num_more_neg+1), 1);
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unused_pos = 0;
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curr_filled = 0;
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for j=imgs_to_use
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labels = squeeze(landmark_locations(j,:,:));
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img = squeeze(all_images(j,:,:));
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if(mirror)
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img = fliplr(img);
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imgSize = size(img);
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flippedLbls = labels;
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flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1) + 1;
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tmp1 = flippedLbls(label_mirror_inds(:,1),:);
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tmp2 = flippedLbls(label_mirror_inds(:,2),:);
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flippedLbls(label_mirror_inds(:,2),:) = tmp1;
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flippedLbls(label_mirror_inds(:,1),:) = tmp2;
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labels = flippedLbls;
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end
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% If for some reason some of the labels are not visible in the
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% current sample skip this label
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non_existent_labels = labels(:,1)==0 | labels(:,2)==0;
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non_existent_inds = find(non_existent_labels)-1;
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if(numel(intersect(triangulation(:), non_existent_inds)) > 0)
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unused_pos = unused_pos + 1;
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continue;
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end
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% Centering the pixel so that 0,0 is center of the top left pixel
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labels = labels - 1;
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curr_filled = curr_filled + 1;
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[features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
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% sample_img = zeros(size(mask));sample_img(mask) = features;imagesc(sample_img)
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examples(curr_filled,:) = features;
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errors(curr_filled,:) = 0;
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% Extract the correct PDM parameters for the model (we will perturb
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% them for some negative examples)
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[ a_orig, R_orig, trans_orig, ~, params_orig] = fit_PDM_ortho_proj_to_2D(M, E, V, labels);
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eul_orig = Rot2Euler(R_orig);
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% a slightly perturbed example, too tight
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% from 0.3 to 0.9
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a_mod = a_orig * (0.6 + (randi(7) - 4)*0.1);
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p_global = [a_mod; eul_orig'; trans_orig];
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labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
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labels_mod = labels_mod(:,1:2);
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[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
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% sample_img = zeros(size(mask));sample_img(mask) = features;imagesc(sample_img)
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curr_filled = curr_filled + 1;
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examples(curr_filled,:) = features;
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% Compute the badness of fit
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error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
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errors(curr_filled,:) = error;
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% a slightly perturbed example, too broad
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% from 1.2 to 0.6
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a_mod = a_orig * (1.4 + (randi(5) - 3)*0.1);
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p_global = [a_mod; eul_orig'; trans_orig];
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labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
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labels_mod = labels_mod(:,1:2);
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[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
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% sample_img = zeros(size(mask));sample_img(mask) = features;imagesc(sample_img)
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curr_filled = curr_filled + 1;
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examples(curr_filled,:) = features;
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error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
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errors(curr_filled,:) = error;
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% A somewhat offset example
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trans_mod = trans_orig + randn(2,1) * 20;
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p_global = [a_orig; eul_orig'; trans_mod];
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labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
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labels_mod = labels_mod(:,1:2);
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[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
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curr_filled = curr_filled + 1;
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examples(curr_filled,:) = features;
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error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
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errors(curr_filled,:) = error;
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% A rotated sample
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eul_mod = eul_orig + randn(1,3)*0.3;
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p_global = [a_orig; eul_mod'; trans_orig];
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labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
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labels_mod = labels_mod(:,1:2);
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[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
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curr_filled = curr_filled + 1;
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examples(curr_filled,:) = features;
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error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
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errors(curr_filled,:) = error;
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% A sample with modified shape parameters
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p_global = [a_orig; eul_orig'; trans_orig];
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params_mod = params_orig + randn(size(params_orig)).*sqrt(E);
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labels_mod = GetShapeOrtho(M, V, params_mod, p_global);
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labels_mod = labels_mod(:,1:2);
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[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
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curr_filled = curr_filled + 1;
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examples(curr_filled,:) = features;
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error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
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errors(curr_filled,:) = error;
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% pick a random image from negative inriaperson dataset, use original location if
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% first, otherwhise resize it to fit
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for n=6:num_more_neg
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n_img = randi(numel(neg_images));
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neg_image = imread([neg_image_loc, neg_images(n_img).name]);
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if(size(neg_image,3) == 3)
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neg_image = rgb2gray(neg_image);
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end
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[h_neg, w_neg] = size(neg_image);
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% if the current labels fit just use them, if not, then resize
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% to fit
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max_x = max(labels(:,1));
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max_y = max(labels(:,2));
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if(max_x > w_neg || max_y > h_neg)
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neg_image = imresize(neg_image, [max_y, max_x]);
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end
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[features] = ExtractFaceFeatures(neg_image, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
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curr_filled = curr_filled + 1;
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examples(curr_filled,:) = features;
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% Set high error to 3
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errors(curr_filled,:) = 3;
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end
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if(mod(curr_filled, 10) == 0)
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fprintf('%d/%d done\n', curr_filled/(num_more_neg+1), numel(imgs_to_use));
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end
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% add the pos example to the background
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end
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examples = examples(1:curr_filled,:);
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errors = errors(1:curr_filled);
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filename = sprintf('%s/face_validator_train_%d.mat', outputLocation, r);
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save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres');
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end
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end
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function [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY)
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% Make sure labels are within range
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[hRes, wRes] = size(img);
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labels(labels(:,1) < 0,1) = 0;
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labels(labels(:,2) < 0,2) = 0;
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labels(labels(:,1) > wRes-1,1) = wRes-1;
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labels(labels(:,2) > hRes-1,2) = hRes-1;
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crop_img = Crop(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
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crop_img(isnan(crop_img)) = 0;
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% vectorised version
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features = reshape(crop_img(logical(mask)), 1, nPix);
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% normalisations
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features = (features - mean(features));
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norms = std(features);
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if(norms==0)
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norms = 1;
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end
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features = features / norms;
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end |