138 lines
4.2 KiB
Matlab
138 lines
4.2 KiB
Matlab
clear
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location = './prep_data/';
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faceCheckersLoc = dir([location 'face_checker_general_training_68_*']);
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training_ratio = 0.7;
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rng(0);
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for i=1:numel(faceCheckersLoc)
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load([location faceCheckersLoc(i).name]);
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num_examples = size(examples, 1);
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training_cutoff = round(num_examples * training_ratio);
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% picking training data for SVM (positive and negative samples)
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examples_train = examples(1:training_cutoff,:);
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% Extract the mean and standard deviation and normalise by it
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mean_ex = mean(examples_train);
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std_ex = std(examples_train);
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examples_train = bsxfun(@times, bsxfun(@minus, examples_train, mean_ex), 1./std_ex);
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errors_train = errors(1:training_cutoff);
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[prin_comps, score, latent] = pca(examples_train);
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% Keep enough data to explain 95 percent of variability
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variance_explained = cumsum(latent) / sum(latent);
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num_components = find(variance_explained > 0.90, 1, 'first');
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prin_comps = prin_comps(:,1:num_components);
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examples_train_ld = prin_comps' * examples_train';
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examples_train_ld = examples_train_ld';
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min_err = min( errors(1:training_cutoff));
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max_err = max( errors(1:training_cutoff));
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labels_train = 2*((errors(1:training_cutoff) - min_err)/(max_err-min_err)-0.5);
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% liblinear SVR training
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addpath('C:\liblinear\matlab');
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cs = [-4:-1];
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ps = [-10:-2];
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cmd = ['-s 11 -B 1 -q '];
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res = zeros(numel(cs), numel(ps));
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for c=1:numel(cs)
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for p=1:numel(ps)
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validation_p = sprintf('%s -v 5 -p %f -c %f', cmd, 10^cs(c), 2^ps(p));
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rms_valid = train(labels_train, sparse(double(examples_train_ld)), validation_p);
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res(c,p) = rms_valid;
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end
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end
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[val,~] = min(min(res));
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[a, b] = ind2sub(size(res), find(res == val));
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best_c = cs(a);
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best_p = ps(b);
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best_params = sprintf('%s -p %f -c %f', cmd, 10^best_c, 2^best_p);
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regressor_lsvr = train(labels_train, sparse(double(examples_train_ld)), best_params);
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w = regressor_lsvr.w(1:end-1)';
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b = regressor_lsvr.w(end);
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examples_test = examples(training_cutoff+1:end,:);
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examples_test = bsxfun(@times, bsxfun(@minus, examples_test, mean_ex), 1./std_ex);
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examples_test_ld = prin_comps' * examples_test';
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examples_test_ld = examples_test_ld';
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labels_test = 2*((errors(training_cutoff+1:end) - min_err)/(max_err-min_err)-0.5);
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dec = examples_test_ld * w + b;
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rmse = sqrt(mean((dec - labels_test).^2));
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corr_dec = corr(dec, labels_test);
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% Need to decide on a threshold for positive and negative
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thresh_corr = -0.85;
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class_test = zeros(size(labels_test));
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class_predict = zeros(size(labels_test));
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class_test(labels_test < thresh_corr) = 1;
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class_test(labels_test >= thresh_corr) = -1;
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class_predict(dec < thresh_corr) = 1;
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class_predict(dec >= thresh_corr) = -1;
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TP = sum(class_predict==1 & class_test == 1);
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FP = sum(class_predict==1 & class_test == -1);
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FN = sum(class_predict==-1 & class_test == 1);
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Precission = TP / (TP+FP);
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Recall = TP / (TP+FN);
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F1 = 2 * (Precission * Recall) / (Precission + Recall);
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faceChecker.corr = corr_dec;
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faceChecker.principal_components = prin_comps;
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faceChecker.mean_ex = mean_ex;
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faceChecker.std_ex = std_ex;
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faceChecker.triangulation = triangulation;
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faceChecker.centres = centres;
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faceChecker.triX = triX;
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faceChecker.nPix = nPix;
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faceChecker.minX = minX;
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faceChecker.minY = minY;
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faceChecker.source = shape(:,1:2);
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faceChecker.mask = mask;
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faceChecker.alphas = alphas;
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faceChecker.betas = betas;
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faceChecker.w = w;
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faceChecker.b = b;
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if(i==1)
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faceCheckers = faceChecker;
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else
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faceCheckers = [faceCheckers faceChecker];
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end
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end
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locationOut = './trained/face_check_general_68.txt';
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locationOutM = './trained/face_check_general_68.mat';
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WriteOutFaceCheckers(locationOut, locationOutM, faceCheckers);
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% as a side effect write out a triangulation file as well
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WriteOutTriangulation('./trained/tris_68.txt', './trained/tris_68.mat', faceCheckers);
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