Generative texture research

simulateImage.py

import Image
import numpy as np
import scipy

# To test the codes put a number between [1,2] in the variable do_code
do_code = 2;

if do_code == 1:
    import kuramotoGrid as kg
    K = 20.0
    x = np.random.uniform(0.0,2*np.pi,(80,80));
    x.reshape(-1);
    # Frequencia dos osciladores, aqui vao as frequencias encontradas na analise da imagem
    w = np.random.uniform(0.0,1.0,(80,80));
    t = scipy.arange(0,200,0.01);

    solv = kg.odeint(kg.kuramotoGrid,x,t,(K,w))

    for i in range(20000):
        img = Image.new('L',(80,80),255)
        last = 255*np.sin(solv[i])
        img.putdata(last)
        img.save("randomFreq"+str(i)+".jpg")

####### Indo e voltado de imagem para array 2D de array 2D para imagem
#img = Image.Image.load('penalva/r.0.jpg')
#img = img.convert('L')
#data = np.array(list(img.getdata()))
#data = data.reshape((80,80))
#data = data.reshape(-1)    # this function is flattering the matrix to a 1D object
#data = data.tolist()
#img = Image.new('L',(80,80),255)
#img.putdata(data)

# Log-energia de fourier de pedacos 10 x 10 de uma figura
#########################################################

elif do_code == 2:
    from scipy import fftpack
    img = Image.open('penalva/r.0.jpg');
    img = img.convert('L');
    s = [img.size[0], img.size[1]];
    pttlen = 80;
    arr = np.array(list(img.getdata()));
    arr = arr.reshape((800,800));
#    energia = dict( [[(i,j), np.log(fftpack.fft2(arr[i*s[0]/pttlen:(i+1)*s[0]/pttlen,j*s[1]/pttlen:(j+1)*s[1]/pttlen]).real**2 + fftpack.fft(arr[i*s[0]/pttlen:(i+1)*s[0]/pttlen,j*s[1]/pttlen:(j+1)*s[1]/pttlen]).imag**2)] for i in range(pttlen) for j in range(pttlen)]);
    meanpix = dict( [[(i,j), np.mean(arr[i*s[0]/pttlen:(i+1)*s[0]/pttlen,j*s[1]/pttlen:(j+1)*s[1]/pttlen])] for i in range(pttlen) for j in range(pttlen)]);
    normpix = float(np.max(meanpix.values()))
    import kuramotoGrid as kg
    K = 0.05
    x = np.random.uniform(0.0,2*np.pi,(pttlen,pttlen));
    x = x.reshape(-1);
    # Frequencia dos osciladores, aqui vao as frequencias encontradas na analise da imagem
    w = np.zeros((pttlen,pttlen))
    for v in meanpix.keys():
        data = meanpix[v];
#        norm = float(max(energia[v].reshape(-1)))
#        for i in range(s[0]/pttlen):
#            for j in range(s[1]/pttlen):
#                if data[i,j]/norm > 0.7:
#                    w[v[0],v[1]] = w[v[0],v[1]] + (i/float(s[0]/pttlen))**2 + (j/float(s[1]/pttlen))**2;
#        w[v[0],v[1]] = 2*np.pi*np.sqrt(w[v[0],v[1]]);
        w[v[0],v[1]] = meanpix[v]/normpix
    t = scipy.arange(0,120,0.01);

    solv = kg.odeint(kg.kuramotoGrid,x,t,(K,w))
      
    freq = np.array([kg.kuramotoGrid(v,0,K,w) for v in solv])
    freq = np.mean(freq, axis = 0);
    freq = 255*freq/np.max(freq)
#    freq = freq.reshape((pttlen,pttlen));
#    print freq
    freq = freq.tolist()    
#    for i in range(10000):

    img = Image.new('L',(80,80),255)
#   freq = 255*np.sin(solv[i])
    img.putdata(freq)
    img.save("PixK_0.05_t120_fig0.jpg")

# This rebuilds the original imagem in the frequency domain of window length s[0]/pttlen x s[1]/pttlen
elif do_code == 3:  
    fourierimage = Image.new('L',(s[0],s[1]),255);
    for v in energia.keys():
        b0 = Image.new('L',(s[0]/pttlen,s[1]/pttlen),255);
        data = energia[v];
        data = data.reshape(-1);
        data = data.tolist();
        b0.putdata(data);
        fourierimage.paste(b0,(v[0]*(s[0]/pttlen),v[1]*(s[1]/pttlen)))
    fourierimage.show();
else:
    print 'Nothing to do, call your friends and go to the beach to drink coconut water and feel what nature and life is really about !!!'