def compute_homographies(self, outlier_algorithm=cv2.RANSAC, clean_keys=[]):
"""
For each edge in the (sub) graph, compute the homography
Parameters
----------
outlier_algorithm : object
An openCV outlier detections algorithm, e.g. cv2.RANSAC
clean_keys : list
of string keys to masking arrays
(created by calling outlier detection)
Returns
-------
transformation_matrix : ndarray
The 3x3 transformation matrix
mask : ndarray
Boolean array of the outliers
"""
for source, destination, attributes in self.edges_iter(data=True):
matches = attributes['matches']
if clean_keys:
mask = np.prod([attributes[i] for i in clean_keys], axis=0, dtype=np.bool)
matches = matches[mask]
full_mask = np.where(mask == True)
s_coords = np.empty((len(matches), 2))
d_coords = np.empty((len(matches), 2))
for i, (idx, row) in enumerate(matches.iterrows()):
s_idx = int(row['source_idx'])
d_idx = int(row['destination_idx'])
s_coords[i] = self.node[source]['keypoints'][s_idx].pt
d_coords[i] = self.node[destination]['keypoints'][d_idx].pt
transformation_matrix, ransac_mask = od.compute_homography(s_coords,
d_coords)
ransac_mask = ransac_mask.ravel()
# Convert the truncated RANSAC mask back into a full length mask
if clean_keys:
mask[full_mask] = ransac_mask
else:
mask = ransac_mask
attributes['homography'] = transformation_matrix
attributes['ransac'] = mask
def compute_homography(self, method='ransac', clean_keys=[], pid=None, **kwargs):
"""
For each edge in the (sub) graph, compute the homography
Parameters
----------
outlier_algorithm : object
An openCV outlier detections algorithm, e.g. cv2.RANSAC
clean_keys : list
of string keys to masking arrays
(created by calling outlier detection)
Returns
-------
transformation_matrix : ndarray
The 3x3 transformation matrix
mask : ndarray
Boolean array of the outliers
"""
if hasattr(self, 'matches'):
matches = self.matches
else:
raise AttributeError('Matches have not been computed for this edge')
if clean_keys:
matches, mask = self._clean(clean_keys)
s_keypoints = self.source.keypoints.iloc[matches['source_idx'].values]
d_keypoints = self.destination.keypoints.iloc[matches['destination_idx'].values]
transformation_matrix, ransac_mask = od.compute_homography(s_keypoints[['x', 'y']].values,
d_keypoints[['x', 'y']].values,
**kwargs)
ransac_mask = ransac_mask.ravel()
# Convert the truncated RANSAC mask back into a full length mask
if clean_keys:
mask[mask == True] = ransac_mask
else:
mask = ransac_mask
self.masks = ('ransac', mask)
self.homography = Homography(transformation_matrix,
s_keypoints[ransac_mask][['x', 'y']],
d_keypoints[ransac_mask][['x', 'y']],
mask=mask[mask == True].index)
# Finalize the array to get custom attrs to propagate
self.homography.__array_finalize__(self.homography)
def compute_homography(self, outlier_algorithm=cv2.RANSAC, clean_keys=[]):
"""
For each edge in the (sub) graph, compute the homography
Parameters
----------
outlier_algorithm : object
An openCV outlier detections algorithm, e.g. cv2.RANSAC
clean_keys : list
of string keys to masking arrays
(created by calling outlier detection)
Returns
-------
transformation_matrix : ndarray
The 3x3 transformation matrix
mask : ndarray
Boolean array of the outliers
"""
if hasattr(self, 'matches'):
matches = self.matches
else:
raise AttributeError(
'Matches have not been computed for this edge')
if clean_keys:
mask = np.prod([self._mask_arrays[i] for i in clean_keys],
axis=0,
dtype=np.bool)
matches = matches[mask]
full_mask = np.where(mask == True)
s_keypoints = self.source.keypoints.iloc[matches['source_idx'].values]
d_keypoints = self.destination.keypoints.iloc[
matches['destination_idx'].values]
transformation_matrix, ransac_mask = od.compute_homography(
s_keypoints[['x', 'y']].values, d_keypoints[['x', 'y']].values)
ransac_mask = ransac_mask.ravel()
# Convert the truncated RANSAC mask back into a full length mask
if clean_keys:
mask[full_mask] = ransac_mask
else:
mask = ransac_mask
self.masks = ('ransac', mask)
self.homography = transformation_matrix
def compute_homography(self, outlier_algorithm=cv2.RANSAC, clean_keys=[]):
"""
For each edge in the (sub) graph, compute the homography
Parameters
----------
outlier_algorithm : object
An openCV outlier detections algorithm, e.g. cv2.RANSAC
clean_keys : list
of string keys to masking arrays
(created by calling outlier detection)
Returns
-------
transformation_matrix : ndarray
The 3x3 transformation matrix
mask : ndarray
Boolean array of the outliers
"""
if hasattr(self, 'matches'):
matches = self.matches
else:
raise AttributeError('Matches have not been computed for this edge')
if clean_keys:
mask = np.prod([self._mask_arrays[i] for i in clean_keys], axis=0, dtype=np.bool)
matches = matches[mask]
full_mask = np.where(mask == True)
s_keypoints = self.source.keypoints.iloc[matches['source_idx'].values]
d_keypoints = self.destination.keypoints.iloc[matches['destination_idx'].values]
transformation_matrix, ransac_mask = od.compute_homography(s_keypoints[['x', 'y']].values,
d_keypoints[['x', 'y']].values)
ransac_mask = ransac_mask.ravel()
# Convert the truncated RANSAC mask back into a full length mask
if clean_keys:
mask[full_mask] = ransac_mask
else:
mask = ransac_mask
self.masks = ('ransac', mask)
self.homography = transformation_matrix
