"""
Image compression via tensor decomposition
==========================================

Example on how to use :func:`tensorly.decomposition.parafac`and :func:`tensorly.decomposition.tucker` on images.
"""

import matplotlib.pyplot as plt
import tensorly as tl
import numpy as np
from scipy.misc import face, imresize
from tensorly.decomposition import parafac
from tensorly.decomposition import tucker
from math import ceil


random_state = 12345

image = tl.tensor(imresize(face(), 0.3), dtype='float64')

def to_image(tensor):
    """A convenience function to convert from a float dtype back to uint8"""
    im = tl.to_numpy(tensor)
    im -= im.min()
    im /= im.max()
    im *= 255
    return im.astype(np.uint8)

# Rank of the CP decomposition
cp_rank = 25
# Rank of the Tucker decomposition
tucker_rank = [100, 100, 2]

# Perform the CP decomposition
weights, factors = parafac(image, rank=cp_rank, init='random', tol=10e-6)
# Reconstruct the image from the factors
cp_reconstruction = tl.kruskal_to_tensor((weights, factors))

# Tucker decomposition
core, tucker_factors = tucker(image, ranks=tucker_rank, init='random', tol=10e-5, random_state=random_state)
tucker_reconstruction = tl.tucker_to_tensor((core, tucker_factors))

# Plotting the original and reconstruction from the decompositions
fig = plt.figure()
ax = fig.add_subplot(1, 3, 1)
ax.set_axis_off()
ax.imshow(to_image(image))
ax.set_title('original')

ax = fig.add_subplot(1, 3, 2)
ax.set_axis_off()
ax.imshow(to_image(cp_reconstruction))
ax.set_title('CP')

ax = fig.add_subplot(1, 3, 3)
ax.set_axis_off()
ax.imshow(to_image(tucker_reconstruction))
ax.set_title('Tucker')

plt.tight_layout()
plt.show()