def example_binary():
# generate trivial data
x = np.ones((10, 12))
x[:, 6:] = -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_qpbo = binary_grid(unaries, pairwise)
#result_qpbo = alpha_expansion_grid(unaries, pairwise)
#result_gc = 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 = binary_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="qpbo")
plt.imshow(result_qpbo, interpolation='nearest')
plt.subplot(236, title="qpbo graph")
plt.imshow(result_graph.reshape(x.shape), interpolation='nearest')
plt.show()
def inference(self, x, w):
if w.shape != (self.size_psi,):
raise ValueError("Got w of wrong shape. Expected %s, got %s" %
(self.size_psi, w.shape))
self.inference_calls += 1
pairwise_flat = np.asarray(w[:-1])
unary = w[-1]
pairwise_params = np.zeros((self.n_states, self.n_states))
pairwise_params[np.tri(self.n_states, k=-1, dtype=np.bool)] = pairwise_flat
pairwise_params = pairwise_params + pairwise_params.T\
- np.diag(np.diag(pairwise_params))
unaries = (-1000 * unary * x).astype(np.int32)
pairwise = (-1000 * pairwise_params).astype(np.int32)
y = alpha_expansion_graph(self.edges, unaries, pairwise, random_seed=1)
from pyqpbo import binary_graph
y_ = binary_graph(self.edges, unaries, pairwise)
if (y_ != y).any():
tracer()
return y
def example_binary():
# generate trivial data
x = np.ones((10, 12))
x[:, 6:] = -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_qpbo = binary_grid(unaries, pairwise)
#result_qpbo = alpha_expansion_grid(unaries, pairwise)
#result_gc = 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 = binary_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="qpbo")
plt.imshow(result_qpbo, interpolation='nearest')
plt.subplot(236, title="qpbo graph")
plt.imshow(result_graph.reshape(x.shape), interpolation='nearest')
plt.show()
