cc.py

# Create n noisy images

n = 10
nclasses = 2
images = [io.imread('ellipse.png',pilmode="L"), io.imread('rectangle.png',pilmode="L")]

# Store the noise in 3D matrices
noisy_images = np.zeros((n,images[0].shape[0], images[0].shape[1]))
classes = np.zeros((n))

# Calculate noisy images the noise

for i in range(n):
  c = np.random.randint(0,nclasses)
  noisy_images[i] = np.random.poisson(images[c])
  classes[i] = c

np.save('classes.npy',classes)


# Calculate correlation coefficient matrix

# You should put all images in one matrix and call corrcoef on it.
cc_matrix = numpy.corrcoef(noisy_images.reshape(n,-1))