geenrating_stats.py

import numpy as np
import numpy.linalg as linalg
import sys
from PIL import Image
import matplotlib.pyplot as plt
from sklearn.metrics import mean_absolute_error
import time


start = time.time()


A,B=[],[]
for i in range(512,300,-1):
    r = i
    Maxiter = 500
    Ro = float('inf')
    t = 0
    image = Image.open('Lenna.png')
    gray_img = image.convert("L")
    M = np.asarray(gray_img) 
    U,S,V = linalg.svd(M)
    Vbarre = V[:,:r]
    Ro = float('inf')
    t = 0
    while 1:
        t += 1
        VbarrePlus = np.where(Vbarre < 0, 0, Vbarre)
        Y = np.dot( Vbarre.T, VbarrePlus)
        Ro0 = Ro
        Ro = linalg.norm(Y-np.identity(Y.shape[0]),'fro')**2
        if(Ro0-Ro < sys.float_info.epsilon) or (t == Maxiter):
            break
        U,S,V= linalg.svd(Y)
        Vbarre = np.dot(Vbarre,np.dot(U,V.T))  
    #S = np.where(Vbarre < 0, 0, Vbarre) #Vbarre Plus
    S = np.dot(Vbarre,Y)
    aux = np.dot(M,Vbarre)
    W = np.dot(aux,linalg.inv(Y.T)) # transposé de la matrice diagonale inferieure de Y
                                    # Il s'agit de l'inverse de la transposée de la matrice Y
    out = np.dot(W,S.T)

    data = Image.fromarray(out)
    #plt.imshow(data, cmap='gray')
    #plt.show()
    new_p = data.convert("L")
    #new_p.save("output"+str(r)+"v2.png")
    loss = mean_absolute_error(out, M)    #mean square error
    A.append(i)
    B.append(loss)
plt.plot(A,B)
plt.gca().invert_xaxis()
plt.show()


end = time.time()
print(end - start)