def merge_two_reconstructions(r1, r2, config, threshold=1):
''' Merge two reconstructions with common tracks
'''
t1, t2 = r1['points'], r2['points']
common_tracks = list(set(t1) & set(t2))
# print 'Number of common tracks between two reconstructions: {0}'.format(len(common_tracks))
if len(common_tracks) > 6:
# Estimate similarity transform
p1 = np.array([t1[t]['coordinates'] for t in common_tracks])
p2 = np.array([t2[t]['coordinates'] for t in common_tracks])
T, inliers = multiview.fit_similarity_transform(p1,
p2,
max_iterations=1000,
threshold=threshold)
if len(inliers) >= 10:
s, A, b = multiview.decompose_similarity_transform(T)
r1p = r1
apply_similarity(r1p, s, A, b)
r = r2
r['shots'].update(r1p['shots'])
r['points'].update(r1p['points'])
align_reconstruction(r, config)
return [r]
else:
return [r1, r2]
else:
return [r1, r2]
def merge_two_reconstructions(r1, r2, config, threshold=1):
"""Merge two reconstructions with common tracks."""
t1, t2 = r1.points, r2.points
common_tracks = list(set(t1) & set(t2))
if len(common_tracks) > 6:
# Estimate similarity transform
p1 = np.array([t1[t].coordinates for t in common_tracks])
p2 = np.array([t2[t].coordinates for t in common_tracks])
T, inliers = multiview.fit_similarity_transform(p1,
p2,
max_iterations=1000,
threshold=threshold)
if len(inliers) >= 10:
s, A, b = multiview.decompose_similarity_transform(T)
r1p = r1
align.apply_similarity(r1p, s, A, b)
r = r2
r.shots.update(r1p.shots)
r.points.update(r1p.points)
align.align_reconstruction(r, None, config)
return [r]
else:
return [r1, r2]
else:
return [r1, r2]
def merge_two_reconstructions(r1, r2, config, threshold=1):
''' Merge two reconstructions with common tracks
'''
t1, t2 = r1.points, r2.points
common_tracks = list(set(t1) & set(t2))
# print 'Number of common tracks between two reconstructions: {0}'.format(len(common_tracks))
if len(common_tracks) > 6:
# Estimate similarity transform
p1 = np.array([t1[t].coordinates for t in common_tracks])
p2 = np.array([t2[t].coordinates for t in common_tracks])
T, inliers = multiview.fit_similarity_transform(p1, p2, max_iterations=1000, threshold=threshold)
if len(inliers) >= 10:
s, A, b = multiview.decompose_similarity_transform(T)
r1p = r1
apply_similarity(r1p, s, A, b)
r = r2
r.shots.update(r1p.shots)
r.points.update(r1p.points)
align_reconstruction(r, config)
return [r]
else:
return [r1, r2]
else:
return [r1, r2]
def align_two_reconstruction(r1, r2, common_tracks, threshold):
"""Estimate similarity transform between two reconstructions."""
t1, t2 = r1.points, r2.points
if len(common_tracks) > 6:
p1 = np.array([t1[t[0]].coordinates for t in common_tracks])
p2 = np.array([t2[t[1]].coordinates for t in common_tracks])
# 3 samples / 100 trials / 50% outliers = 0.99 probability
# with probability = 1-(1-(1-outlier)^model)^trial
T, inliers = multiview.fit_similarity_transform(
p1, p2, max_iterations=100, threshold=threshold)
if len(inliers) > 0:
return True, T, inliers
return False, None, None
def align_two_reconstruction(
r1: types.Reconstruction,
r2: types.Reconstruction,
common_tracks: List[Tuple[str, str]],
threshold: float,
) -> Tuple[float, Optional[np.ndarray], List[int]]:
"""Estimate similarity transform between two reconstructions."""
t1, t2 = r1.points, r2.points
if len(common_tracks) > 6:
p1 = np.array([t1[t[0]].coordinates for t in common_tracks])
p2 = np.array([t2[t[1]].coordinates for t in common_tracks])
# 3 samples / 100 trials / 50% outliers = 0.99 probability
# with probability = 1-(1-(1-outlier)^model)^trial
T, inliers = multiview.fit_similarity_transform(
p1, p2, max_iterations=100, threshold=threshold
)
if len(inliers) > 0:
return True, T, inliers
return False, None, []
