def compute_fundamental_matrix(self, clean_keys=[], **kwargs):
"""
Estimate the fundamental matrix (F) using the correspondences tagged to this
edge.
Parameters
----------
clean_keys : list
Of strings used to apply masks to omit correspondences
method : {linear, nonlinear}
Method to use to compute F. Linear is significantly faster at
the cost of reduced accuracy.
See Also
--------
autocnet.transformation.transformations.FundamentalMatrix
:
"""
if not hasattr(self, 'matches'):
raise AttributeError(
'Matches have not been computed for this edge')
return
matches, mask = self._clean(clean_keys)
# TODO: Homogeneous is horribly inefficient here, use Numpy array notation
s_keypoints = self.source.get_keypoint_coordinates(
index=matches['source_idx'], homogeneous=True)
d_keypoints = self.destination.get_keypoint_coordinates(
index=matches['destination_idx'], homogeneous=True)
# Replace the index with the matches index.
s_keypoints.index = matches.index
d_keypoints.index = matches.index
self.fundamental_matrix = FundamentalMatrix(np.zeros((3, 3)),
index=matches.index)
self.fundamental_matrix.compute(s_keypoints, d_keypoints, **kwargs)
# Convert the truncated RANSAC mask back into a full length mask
mask[mask] = self.fundamental_matrix.mask
# Subscribe the health watcher to the fundamental matrix observable
self.fundamental_matrix.subscribe(self._health.update)
self.fundamental_matrix._notify_subscribers(self.fundamental_matrix)
# Set the initial state of the fundamental mask in the masks
self.masks = ('fundamental', mask)
