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import matplotlib.pyplot as plt

import numpy as np

from pygco import cut_simple, cut_from_graph

def example_binary():

# generate trivial data

x = np.ones((10, 10))

x[:, 5:] = -1

x_noisy = x + np.random.normal(0, 0.8, size=x.shape)

x_thresh = x_noisy > .0

# create unaries

unaries = x_noisy

# as we convert to int, we need to multipy to get sensible values

unaries = (10 * np.dstack([unaries, -unaries]).copy("C")).astype(np.int32)

# create potts pairwise

pairwise = -10 * np.eye(2, dtype=np.int32)

# do simple cut

result = cut_simple(unaries, pairwise)

# use the gerneral graph algorithm

# first, we construct the grid graph

inds = np.arange(x.size).reshape(x.shape)

horz = np.c_[inds[:, :-1].ravel(), inds[:, 1:].ravel()]

vert = np.c_[inds[:-1, :].ravel(), inds[1:, :].ravel()]

edges = np.vstack([horz, vert]).astype(np.int32)

# we flatten the unaries

result_graph = cut_from_graph(edges, unaries.reshape(-1, 2), pairwise)

# plot results

plt.subplot(231, title="original")

plt.imshow(x, interpolation='nearest')

plt.subplot(232, title="noisy version")

plt.imshow(x_noisy, interpolation='nearest')

plt.subplot(233, title="rounded to integers")

plt.imshow(unaries[:, :, 0], interpolation='nearest')

plt.subplot(234, title="thresholding result")

plt.imshow(x_thresh, interpolation='nearest')

plt.subplot(235, title="cut_simple")

plt.imshow(result, interpolation='nearest')

plt.subplot(236, title="cut_from_graph")

plt.imshow(result_graph.reshape(x.shape), interpolation='nearest')

plt.show()

def example_multinomial():

# generate dataset with three stripes

np.random.seed(15)

x = np.zeros((10, 12, 3))

x[:, :4, 0] = -1

x[:, 4:8, 1] = -1

x[:, 8:, 2] = -1

unaries = x + 1.5 * np.random.normal(size=x.shape)

x = np.argmin(x, axis=2)

unaries = (unaries * 10).astype(np.int32)

x_thresh = np.argmin(unaries, axis=2)

# potts potential

pairwise_potts = -2 * np.eye(3, dtype=np.int32)

result = cut_simple(unaries, 10 * pairwise_potts)

# potential that penalizes 0-1 and 1-2 less thann 0-2

pairwise_1d = -15 * np.eye(3, dtype=np.int32) - 8

pairwise_1d[-1, 0] = 0

pairwise_1d[0, -1] = 0

print(pairwise_1d)

result_1d = cut_simple(unaries, pairwise_1d)

plt.subplot(141, title="original")

plt.imshow(x, interpolation="nearest")

plt.subplot(142, title="thresholded unaries")

plt.imshow(x_thresh, interpolation="nearest")

plt.subplot(143, title="potts potentials")

plt.imshow(result, interpolation="nearest")

plt.subplot(144, title="1d topology potentials")

plt.imshow(result_1d, interpolation="nearest")

plt.show()

example_binary()

example_multinomial()