def images(
self,
columns=1,
font_scale=1.0,
line_width=3,
figure_size=(8.0, 6.0),
):
from paderbox.visualization import axes_context
from paderbox.visualization.context_manager import _AxesHandler
with axes_context(
columns=columns,
font_scale=font_scale,
line_width=line_width,
figure_size=figure_size,
) as axes:
axes: _AxesHandler
for k in self.review['images']:
axes.new.grid(False) # set gca
self.image(k)
def calculate_mapping(self, mask, plot=False):
"""Returns just the mapping based on permuted mask input.
Args:
mask: Permuted mask with shape (K, F, T).
Returns:
Reverse mapping with shape (K, F).
"""
K, F, _ = mask.shape
assert F % 2 == 1, (F, 'Sure? Usually F is odd.')
# (K, F, T)
# features = mask / np.linalg.norm(mask, axis=-1, keepdims=True)
if self.similarity_metric in ['cos']:
features = _parameterized_vector_norm(mask, axis=-1)
else:
features = mask.copy()
mapping = np.repeat(np.arange(K)[:, None], F, axis=1)
if plot:
from paderbox import visualization as vis
# visualization import plot, axes_context
tmp_mask = apply_mapping(features, mapping)
with vis.axes_context(K + 1) as axes:
for tmp_mask_k in tmp_mask:
vis.plot.mask(
tmp_mask_k.T,
ax=axes.new,
title=f'start',
)
vis.plot.mask(mapping, limits=None, ax=axes.new)
for iterations, start, end in self.alignment_plan:
for iteration in range(iterations):
# (K, T)
time_centroid = np.mean(features[:, start:end, :], axis=1)
if self.similarity_metric in ['cos']:
time_centroid = _parameterized_vector_norm(
time_centroid,
axis=-1,
)
nothing_changed = True
for f in range(start, end):
reverse_permutation = self._align_segment(
features[:, f, :],
time_centroid,
)
if not (reverse_permutation == list(range(K))).all():
nothing_changed = False
features[:, f, :] = features[reverse_permutation, f, :]
mapping[:, f] = mapping[reverse_permutation, f]
if nothing_changed:
break
return mapping