58 lines
1.6 KiB
Mathematica
58 lines
1.6 KiB
Mathematica
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close all
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%acceptable threshold values: 15-30
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threshold = 20;
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cmax = 10;
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cmin = -cmax;
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n_classes = 16;
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iterations = 50;
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N = 200;
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dataset = dir('../images/*/*.jpg');
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dataset_size = length(dataset);
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vecteurs=zeros(2*(cmax-cmin+1),dataset_size); %TODO: renommer en vectors
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% c'est lent
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% s'assurer que l'on choisit toutes les images
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%
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for n=1:dataset_size
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% choix d'une image aléatoire
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choix = 1 + floor(dataset_size*rand(dataset_size, 1));
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% extraction de l'image du dataset
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image = dataset(choix(1));
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% lecture de l'image
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img = imread([image.folder '/' image.name]);
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% filtrage de la couleur de la peau
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binary = rgb_filter(img, threshold);
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% détermination du contour
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c = contourc(binary);
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% Détermination du contour de taille max
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cont = max_contour(c);
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% transformation en signal complex
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z = cont(:,1) + 1i*cont(:,2);
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% calcul des descripteurs de Fourrier
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[coeff,ncoeff]=descripteurfouriernorm(z,cmax);
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% Extraction des composantes
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vecteurs(:,n)=[real(coeff);imag(coeff)];
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% affichage de l'avancement
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disp(n/dataset_size);
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end
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% utilisation de l'algorithme des kmeans
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kmeans = kmoyennes(vecteurs, n_classes, iterations);
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% affichage des prototypes
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figure
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for n=1:n_classes
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contfil=resconstrdesfour(kmeans(1:end/2,n)+1i*kmeans(end/2+1:end,n),N,cmax);
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subplot(4,4,n)
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h=plot(real(contfil),imag(contfil),'-',real(contfil(1)),imag(contfil(1)),'o');
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title(['prototype ' int2str(n)])
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set(h(1),'LineWidth',2)
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set(h(2),'LineWidth',3)
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grid on
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axis equal
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axis ij
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drawnow
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end
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