def align_reconstruction_naive_similarity(config, reconstruction, gcp):
"""Align with GPS and GCP data using direct 3D-3D matches."""
X, Xp = alignment_constraints(config, reconstruction, gcp)
if len(X) == 0:
return 1.0, np.identity(3), np.zeros((3))
# Translation-only case, either :
# - a single value
# - identical values
same_values = (np.linalg.norm(np.std(Xp, axis=0)) < 1e-10)
single_value = (len(X) == 1)
if single_value:
logger.warning('Only 1 constraints. Using translation-only alignment.')
if same_values:
logger.warning('GPS/GCP data seems to have identical values. Using translation-only alignment.')
if same_values or single_value:
t = np.array(Xp[0]) - np.array(X[0])
return 1.0, np.identity(3), t
# Will be up to some unknown rotation
if len(X) == 2:
logger.warning('Only 2 constraints. Will be up to some unknown rotation.')
X.append(X[1])
Xp.append(Xp[1])
# Compute similarity Xp = s A X + b
X = np.array(X)
Xp = np.array(Xp)
T = tf.superimposition_matrix(X.T, Xp.T, scale=True)
A, b = T[:3, :3], T[:3, 3]
s = np.linalg.det(A)**(1. / 3)
A /= s
return s, A, b
def align_reconstruction_naive_similarity(reconstruction, gcp):
"""Align with GPS and GCP data using direct 3D-3D matches."""
X, Xp = [], []
# Get Ground Control Point correspondences
if gcp:
triangulated, measured = triangulate_all_gcp(reconstruction, gcp)
X.extend(triangulated)
Xp.extend(measured)
# Get camera center correspondences
for shot in reconstruction.shots.values():
X.append(shot.pose.get_origin())
Xp.append(shot.metadata.gps_position)
if len(X) < 3:
return
# Compute similarity Xp = s A X + b
X = np.array(X)
Xp = np.array(Xp)
T = tf.superimposition_matrix(X.T, Xp.T, scale=True)
A, b = T[:3, :3], T[:3, 3]
s = np.linalg.det(A)**(1. / 3)
A /= s
return s, A, b
def align_reconstruction_naive_similarity(reconstruction, gcp):
"""Align with GPS and GCP data using direct 3D-3D matches."""
X, Xp = [], []
# Get Ground Control Point correspondences
if gcp:
triangulated, measured = triangulate_all_gcp(reconstruction, gcp)
X.extend(triangulated)
Xp.extend(measured)
# Get camera center correspondences
for shot in reconstruction.shots.values():
X.append(shot.pose.get_origin())
Xp.append(shot.metadata.gps_position)
if len(X) < 3:
return
# Compute similarity Xp = s A X + b
X = np.array(X)
Xp = np.array(Xp)
T = tf.superimposition_matrix(X.T, Xp.T, scale=True)
A, b = T[:3, :3], T[:3, 3]
s = np.linalg.det(A)**(1. / 3)
A /= s
return s, A, b
def align_reconstruction_naive_similarity(config, reconstruction, gcp):
"""Align with GPS and GCP data using direct 3D-3D matches."""
X, Xp = alignment_constraints(config, reconstruction, gcp)
if len(X) == 0:
return 1.0, np.identity(3), np.zeros((3))
# Translation-only case
if len(X) == 1:
logger.warning('Only 1 constraints. Using translation-only alignment.')
t = np.array(Xp[0]) - np.array(X[0])
return 1.0, np.identity(3), t
# Will be up to some unknown rotation
if len(X) == 2:
logger.warning(
'Only 2 constraints. Will be up to some unknown rotation.')
X.append(X[1])
Xp.append(Xp[1])
# Compute similarity Xp = s A X + b
X = np.array(X)
Xp = np.array(Xp)
T = tf.superimposition_matrix(X.T, Xp.T, scale=True)
A, b = T[:3, :3], T[:3, 3]
s = np.linalg.det(A)**(1. / 3)
A /= s
return s, A, b
def get_sab_3d(reconstruction, filtered_shots, gcp, config):
X, Xp = [], []
# Get Ground Control Point correspondences
if gcp and config['align_use_gcp']:
triangulated, measured = triangulate_all_gcp(reconstruction, gcp)
X.extend(triangulated)
Xp.extend(measured)
# Get camera center correspondences
if config['align_use_gps']:
for shot in filtered_shots:
X.append(shot.pose.get_origin())
Xp.append(shot.metadata.gps_position)
# Compute similarity Xp = s A X + b
X = np.array(X)
Xp = np.array(Xp)
T = tf.superimposition_matrix(X.T, Xp.T, scale=True)
A, b = T[:3, :3], T[:3, 3]
s = np.linalg.det(A)**(1. / 3)
A /= s
return s, A, b
def align_reconstruction_naive_similarity(reconstruction):
if len(reconstruction['shots']) < 3: return
# Compute similarity Xp = s A X + b
X, Xp = [], []
for shot in reconstruction['shots'].values():
X.append(optical_center(shot))
Xp.append(shot['gps_position'])
X = np.array(X)
Xp = np.array(Xp)
T = tf.superimposition_matrix(X.T, Xp.T, scale=True)
A, b = T[:3, :3], T[:3, 3]
s = np.linalg.det(A)**(1. / 3)
A /= s
return s, A, b
def align_reconstruction_naive_similarity(reconstruction):
if len(reconstruction['shots']) < 3: return
# Compute similarity Xp = s A X + b
X, Xp = [], []
for shot in reconstruction['shots'].values():
X.append(optical_center(shot))
Xp.append(shot['gps_position'])
X = np.array(X)
Xp = np.array(Xp)
T = tf.superimposition_matrix(X.T, Xp.T, scale=True)
A, b = T[:3,:3], T[:3,3]
s = np.linalg.det(A)**(1./3)
A /= s
return s, A, b
def align_reconstruction_naive_similarity(reconstruction):
if len(reconstruction.shots) < 3: return
# Compute similarity Xp = s A X + b
X, Xp = [], []
for shot in reconstruction.shots.values():
X.append(shot.pose.get_origin())
Xp.append(shot.metadata.gps_position)
X = np.array(X)
Xp = np.array(Xp)
T = tf.superimposition_matrix(X.T, Xp.T, scale=True)
A, b = T[:3, :3], T[:3, 3]
s = np.linalg.det(A)**(1. / 3)
A /= s
return s, A, b
def align_reconstruction_naive_similarity(reconstruction):
if len(reconstruction.shots) < 3: return
# Compute similarity Xp = s A X + b
X, Xp = [], []
for shot in reconstruction.shots.values():
X.append(shot.pose.get_origin())
Xp.append(shot.metadata.gps_position)
X = np.array(X)
Xp = np.array(Xp)
T = tf.superimposition_matrix(X.T, Xp.T, scale=True)
A, b = T[:3,:3], T[:3,3]
s = np.linalg.det(A)**(1./3)
A /= s
return s, A, b
def compute_naive_similarity(
config: Dict[str, Any],
reconstruction: types.Reconstruction,
gcp: List[pymap.GroundControlPoint],
use_gps: bool,
use_scale: bool,
) -> Optional[Tuple[float, np.ndarray, np.ndarray]]:
"""Compute similarity with GPS and GCP data using direct 3D-3D matches."""
X, Xp = alignment_constraints(config, reconstruction, gcp, use_gps)
if len(X) == 0:
return None
# Translation-only case, either :
# - a single value
# - identical values
same_values = np.linalg.norm(np.std(Xp, axis=0)) < 1e-10
single_value = len(X) == 1
if single_value:
logger.warning("Only 1 constraints. Using translation-only alignment.")
if same_values:
logger.warning(
"GPS/GCP data seems to have identical values. Using translation-only alignment."
)
if same_values or single_value:
t = np.array(Xp[0]) - np.array(X[0])
return 1.0, np.identity(3), t
# Will be up to some unknown rotation
if len(X) == 2:
logger.warning(
"Only 2 constraints. Will be up to some unknown rotation.")
X.append(X[1])
Xp.append(Xp[1])
# Compute similarity Xp = s A X + b
X = np.array(X)
Xp = np.array(Xp)
T = tf.superimposition_matrix(X.T, Xp.T, scale=use_scale)
A, b = T[:3, :3], T[:3, 3]
s = np.linalg.det(A)**(1.0 / 3)
A /= s
return s, A, b
