cedalion.geometry.registration
Registrating optodes to scalp surfaces.
Functions
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Selects three points that are spread apart from each other in the dataset. |
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Calculate the affine transformation matrix T that transforms p1 to p2. |
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Perform Iterative Closest Point algorithm with full transformation capabilities. |
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Iterative Closest Point algorithm for registration. |
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Finds affine transformation between coords_target and coords_trafo. |
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Finds affine transformation between coords_target and coords_trafo. |
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Finds affine transformation between coords_target and coords_trafo. |
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Projects 3D coordinates onto a 2D plane using a simple scalp projection. |
- cedalion.geometry.registration.register_trans_rot(
- coords_target: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')],
- coords_trafo: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')],
Finds affine transformation between coords_target and coords_trafo.
Uses translation and roatation rotation. Requires at least 3 common labels between the two point clouds.
- Parameters:
coords_target (LabeledPointCloud) – Target point cloud.
coords_trafo (LabeledPointCloud) – Source point cloud.
- Returns:
Affine transformation between the two point clouds.
- Return type:
cdt.AffineTransform
- cedalion.geometry.registration.register_trans_rot_isoscale(
- coords_target: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')],
- coords_trafo: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')],
Finds affine transformation between coords_target and coords_trafo.
Uses translation, rotation and isotropic scaling. Requires at least 3 common labels between the two point clouds.
- Parameters:
coords_target (LabeledPointCloud) – Target point cloud.
coords_trafo (LabeledPointCloud) – Source point cloud.
- Returns:
Affine transformation between the two point clouds.
- Return type:
cdt.AffineTransform
- cedalion.geometry.registration.register_trans_rot_scale(
- coords_target: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')],
- coords_trafo: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')],
Finds affine transformation between coords_target and coords_trafo.
Uses translation, rotation and scaling. Requires at least 3 common labels between the two point clouds.
- Parameters:
coords_target (LabeledPointCloud) – Target point cloud.
coords_trafo (LabeledPointCloud) – Source point cloud.
- Returns:
Affine transformation between the two point clouds.
- Return type:
cdt.AffineTransform
- cedalion.geometry.registration.gen_xform_from_pts(p1: ndarray, p2: ndarray) ndarray[source]
Calculate the affine transformation matrix T that transforms p1 to p2.
- Parameters:
p1 (np.ndarray) – Source points (p x m) where p is the number of points and m is the number of dimensions.
p2 (np.ndarray) – Target points (p x m) where p is the number of points and m is the number of dimensions.
- Returns:
Affine transformation matrix T.
- cedalion.geometry.registration.register_icp(
- surface: Surface,
- landmarks: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')],
- geo3d: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')],
- niterations=1000,
- random_sample_fraction=0.5,
Iterative Closest Point algorithm for registration.
- Parameters:
surface (Surface) – Surface mesh to which to register the points.
landmarks (LabeledPointCloud) – Landmarks to use for registration.
geo3d (LabeledPointCloud) – Points to register to the surface.
niterations (int) – Number of iterations for the ICP algorithm (default 1000).
random_sample_fraction (float) – Fraction of points to use in each iteration (default 0.5).
- Returns:
Tuple containing the losses and transformations
- Return type:
Tuple[np.ndarray, np.ndarray]
- cedalion.geometry.registration.icp_with_full_transform(
- opt_centers: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')],
- montage_points: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')],
- max_iterations: int = 50,
- tolerance: float = 500.0,
Perform Iterative Closest Point algorithm with full transformation capabilities.
- Parameters:
opt_centers – Source point cloud for alignment.
montage_points – Target reference point cloud.
max_iterations – Maximum number of iterations for convergence.
tolerance – Tolerance for convergence check.
- Returns:
- Transformed source points as a numpy array with their coordinates
updated to reflect the best alignment.
- np.ndarray: Transformation parameters array consisting of
[tx, ty, tz, rx, ry, rz, sx, sy, sz], where ‘t’ stands for translation components, ‘r’ for rotation components (in radians), and ‘s’ for scaling components.
- np.ndarray: Indices of the target points that correspond to each source point as
per the nearest neighbor search.
- Return type:
np.ndarray
- cedalion.geometry.registration.find_spread_points(points_xr: DataArray) ndarray[source]
Selects three points that are spread apart from each other in the dataset.
- Parameters:
points_xr – An xarray DataArray containing the points from which to select.
- Returns:
Indices of the initial, farthest, and median-distanced points from the initial point as determined by their positions in the original dataset.
- cedalion.geometry.registration.simple_scalp_projection(
- geo3d: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')],
Projects 3D coordinates onto a 2D plane using a simple scalp projection.
- Parameters:
geo3d (LabeledPointCloud) – 3D coordinates of points to project. Requires the landmarks Nz, LPA, and RPA.
- Returns:
A LabeledPointCloud containing the 2D coordinates of the projected points.