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<article id="content">
<header>
<h1 class="title">Module <code>pymskt.mesh.meshRegistration</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">import sys
import vtk
try:
import pyfocusr
except ModuleNotFoundError:
print('pyfocusr not found')
print('If you are not using the registration tools, you can ignore this message.')
print('install pyfocusr as described in the README: https://github.com/gattia/pymskt')
print('or visit the pyfocusr github repo: https://github.com/gattia/pyfocusr')
import numpy as np
def get_icp_transform(source, target, max_n_iter=1000, n_landmarks=1000, reg_mode='similarity'):
"""
Get the Interative Closest Point (ICP) transformation from the `source` mesh to the
`target` mesh.
Parameters
----------
source : vtk.vtkPolyData
Source mesh that we want to transform onto the target mesh.
target : vtk.vtkPolyData
Target mesh that we want to transform the source mesh onto.
max_n_iter : int, optional
Max number of iterations for the registration algorithm to perform, by default 1000
n_landmarks : int, optional
How many landmarks to sample when determining distance between meshes &
solving for the optimal transformation, by default 1000
reg_mode : str, optional
The type of registration to perform. The options are:
- 'rigid': true rigid, translation only
- 'similarity': rigid + equal scale
by default 'similarity'
Returns
-------
vtk.vtkIterativeClosestPointTransform
The actual transform object after running the registration.
"""
icp = vtk.vtkIterativeClosestPointTransform()
icp.SetSource(source)
icp.SetTarget(target)
if reg_mode == 'rigid':
icp.GetLandmarkTransform().SetModeToRigidBody()
elif reg_mode == 'similarity':
icp.GetLandmarkTransform().SetModeToSimilarity()
icp.SetMaximumNumberOfIterations(max_n_iter)
icp.StartByMatchingCentroidsOn()
icp.Modified()
icp.Update()
icp.SetMaximumNumberOfLandmarks(n_landmarks)
return icp
def non_rigidly_register(
target_mesh=None,
source_mesh=None,
final_pt_location='weighted_average', # 'weighted_average' or 'nearest_neighbour'
icp_register_first=True, # Get bones/objects into roughly the same alignment first
icp_registration_mode='similarity', # similarity = rigid + scaling (isotropic), ("rigid", "similarity", "affine")
icp_reg_target_to_source=True, # For shape models, the source is usually the reference so we want target in its space (true)
n_spectral_features=3,
n_extra_spectral=3, # For ensuring we have the right spec coords - determined using wasserstein distances.
target_eigenmap_as_reference=True,
get_weighted_spectral_coords=False,
list_features_to_calc=['curvature'], # 'curvature', min_curvature' 'max_curvature' (other features for registration)
use_features_as_coords=True, # During registraiton - do we want to use curvature etc.
rigid_reg_max_iterations=100,
non_rigid_alpha=0.01,
non_rigid_beta=50,
non_rigid_n_eigens=100, # number of eigens for low rank CPD registration
non_rigid_max_iterations=500,
rigid_before_non_rigid_reg=False, # This is of the spectral coordinates - not the x/y/z used in icp_register_first
projection_smooth_iterations=30, # Used for distributing registered points onto target surface - helps preserve diffeomorphism
graph_smoothing_iterations=300, # For smoothing the target mesh before final point correspondence
feature_smoothing_iterations=30, # how much should features (curvature) be smoothed before registration
include_points_as_features=False, # Do we want to incldue x/y/z positions in registration?
norm_physical_and_spectral=True, # set standardized mean and variance for each feature
feature_weights=np.diag([.1,.1]), # should we weight the extra features (curvature) more/less than spectral
n_coords_spectral_ordering=20000, # How many points on mesh to use for ordering spectral coordinates ()
n_coords_spectral_registration=1000, # How many points to use for spectral registrtaion (usually random subsample)
initial_correspondence_type='kd', # kd = nearest neightbor, hungarian = minimum cost of assigning between graphs (more compute heavy)
final_correspondence_type='kd' # kd = nearest neightbor, hungarian = minimum cost of assigning between graphs (more compute heavy)
):
if 'pyfocusr' not in sys.modules:
raise ModuleNotFoundError('pyfocusr is not installed & is necessary for non-rigid registration.')
if final_pt_location not in ['weighted_average', 'nearest_neighbour']:
raise Exception('Did not specify appropriate final_pt_location, must be either "weighted_average", or "nearest_neighbour"')
# Test if mesh is a vtk mesh, or a pymsky.Mesh object.
if isinstance(target_mesh, vtk.vtkPolyData):
vtk_mesh_target = target_mesh
else:
try:
vtk_mesh_target = target_mesh.mesh
except:
raise Exception(f'expected type vtk.vtkPolyData or pymskt.mesh.Mesh, got: {type(target_mesh)}')
if isinstance(source_mesh, vtk.vtkPolyData):
vtk_mesh_source = source_mesh
else:
try:
vtk_mesh_source = source_mesh.mesh
except:
raise Exception(f'expected type vtk.vtkPolyData or pymskt.mesh.Mesh, got: {type(target_mesh)}')
reg = pyfocusr.Focusr(
vtk_mesh_target=vtk_mesh_target,
vtk_mesh_source=vtk_mesh_source,
icp_register_first=icp_register_first,
icp_registration_mode=icp_registration_mode,
icp_reg_target_to_source=icp_reg_target_to_source,
n_spectral_features=n_spectral_features,
n_extra_spectral=n_extra_spectral,
target_eigenmap_as_reference=target_eigenmap_as_reference,
get_weighted_spectral_coords=get_weighted_spectral_coords,
list_features_to_calc=list_features_to_calc,
use_features_as_coords=use_features_as_coords,
rigid_reg_max_iterations=rigid_reg_max_iterations,
non_rigid_alpha=non_rigid_alpha,
non_rigid_beta=non_rigid_beta,
non_rigid_n_eigens=non_rigid_n_eigens,
non_rigid_max_iterations=non_rigid_max_iterations,
rigid_before_non_rigid_reg=rigid_before_non_rigid_reg,
projection_smooth_iterations=projection_smooth_iterations,
graph_smoothing_iterations=graph_smoothing_iterations,
feature_smoothing_iterations=feature_smoothing_iterations,
include_points_as_features=include_points_as_features,
norm_physical_and_spectral=norm_physical_and_spectral,
feature_weights=feature_weights,
n_coords_spectral_ordering=n_coords_spectral_ordering,
n_coords_spectral_registration=n_coords_spectral_registration,
initial_correspondence_type=initial_correspondence_type,
final_correspondence_type=final_correspondence_type
)
reg.align_maps()
if final_pt_location == 'weighted_average':
reg.get_source_mesh_transformed_weighted_avg()
mesh_transformed_to_target = reg.weighted_avg_transformed_mesh
elif final_pt_location == 'nearest_neighbour':
reg.get_source_mesh_transformed_nearest_neighbour()
mesh_transformed_to_target = reg.nearest_neighbour_transformed_mesh
return mesh_transformed_to_target </code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="pymskt.mesh.meshRegistration.get_icp_transform"><code class="name flex">
<span>def <span class="ident">get_icp_transform</span></span>(<span>source, target, max_n_iter=1000, n_landmarks=1000, reg_mode='similarity')</span>
</code></dt>
<dd>
<div class="desc"><p>Get the Interative Closest Point (ICP) transformation from the <code>source</code> mesh to the
<code>target</code> mesh. </p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>source</code></strong> : <code>vtk.vtkPolyData</code></dt>
<dd>Source mesh that we want to transform onto the target mesh.</dd>
<dt><strong><code>target</code></strong> : <code>vtk.vtkPolyData</code></dt>
<dd>Target mesh that we want to transform the source mesh onto.</dd>
<dt><strong><code>max_n_iter</code></strong> : <code>int</code>, optional</dt>
<dd>Max number of iterations for the registration algorithm to perform, by default 1000</dd>
<dt><strong><code>n_landmarks</code></strong> : <code>int</code>, optional</dt>
<dd>How many landmarks to sample when determining distance between meshes &
solving for the optimal transformation, by default 1000</dd>
<dt><strong><code>reg_mode</code></strong> : <code>str</code>, optional</dt>
<dd>The type of registration to perform. The options are:
- 'rigid': true rigid, translation only
- 'similarity': rigid + equal scale
by default 'similarity'</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>vtk.vtkIterativeClosestPointTransform</code></dt>
<dd>The actual transform object after running the registration.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_icp_transform(source, target, max_n_iter=1000, n_landmarks=1000, reg_mode='similarity'):
"""
Get the Interative Closest Point (ICP) transformation from the `source` mesh to the
`target` mesh.
Parameters
----------
source : vtk.vtkPolyData
Source mesh that we want to transform onto the target mesh.
target : vtk.vtkPolyData
Target mesh that we want to transform the source mesh onto.
max_n_iter : int, optional
Max number of iterations for the registration algorithm to perform, by default 1000
n_landmarks : int, optional
How many landmarks to sample when determining distance between meshes &
solving for the optimal transformation, by default 1000
reg_mode : str, optional
The type of registration to perform. The options are:
- 'rigid': true rigid, translation only
- 'similarity': rigid + equal scale
by default 'similarity'
Returns
-------
vtk.vtkIterativeClosestPointTransform
The actual transform object after running the registration.
"""
icp = vtk.vtkIterativeClosestPointTransform()
icp.SetSource(source)
icp.SetTarget(target)
if reg_mode == 'rigid':
icp.GetLandmarkTransform().SetModeToRigidBody()
elif reg_mode == 'similarity':
icp.GetLandmarkTransform().SetModeToSimilarity()
icp.SetMaximumNumberOfIterations(max_n_iter)
icp.StartByMatchingCentroidsOn()
icp.Modified()
icp.Update()
icp.SetMaximumNumberOfLandmarks(n_landmarks)
return icp</code></pre>
</details>
</dd>
<dt id="pymskt.mesh.meshRegistration.non_rigidly_register"><code class="name flex">
<span>def <span class="ident">non_rigidly_register</span></span>(<span>target_mesh=None, source_mesh=None, final_pt_location='weighted_average', icp_register_first=True, icp_registration_mode='similarity', icp_reg_target_to_source=True, n_spectral_features=3, n_extra_spectral=3, target_eigenmap_as_reference=True, get_weighted_spectral_coords=False, list_features_to_calc=['curvature'], use_features_as_coords=True, rigid_reg_max_iterations=100, non_rigid_alpha=0.01, non_rigid_beta=50, non_rigid_n_eigens=100, non_rigid_max_iterations=500, rigid_before_non_rigid_reg=False, projection_smooth_iterations=30, graph_smoothing_iterations=300, feature_smoothing_iterations=30, include_points_as_features=False, norm_physical_and_spectral=True, feature_weights=array([[0.1, 0. ],
[0. , 0.1]]), n_coords_spectral_ordering=20000, n_coords_spectral_registration=1000, initial_correspondence_type='kd', final_correspondence_type='kd')</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def non_rigidly_register(
target_mesh=None,
source_mesh=None,
final_pt_location='weighted_average', # 'weighted_average' or 'nearest_neighbour'
icp_register_first=True, # Get bones/objects into roughly the same alignment first
icp_registration_mode='similarity', # similarity = rigid + scaling (isotropic), ("rigid", "similarity", "affine")
icp_reg_target_to_source=True, # For shape models, the source is usually the reference so we want target in its space (true)
n_spectral_features=3,
n_extra_spectral=3, # For ensuring we have the right spec coords - determined using wasserstein distances.
target_eigenmap_as_reference=True,
get_weighted_spectral_coords=False,
list_features_to_calc=['curvature'], # 'curvature', min_curvature' 'max_curvature' (other features for registration)
use_features_as_coords=True, # During registraiton - do we want to use curvature etc.
rigid_reg_max_iterations=100,
non_rigid_alpha=0.01,
non_rigid_beta=50,
non_rigid_n_eigens=100, # number of eigens for low rank CPD registration
non_rigid_max_iterations=500,
rigid_before_non_rigid_reg=False, # This is of the spectral coordinates - not the x/y/z used in icp_register_first
projection_smooth_iterations=30, # Used for distributing registered points onto target surface - helps preserve diffeomorphism
graph_smoothing_iterations=300, # For smoothing the target mesh before final point correspondence
feature_smoothing_iterations=30, # how much should features (curvature) be smoothed before registration
include_points_as_features=False, # Do we want to incldue x/y/z positions in registration?
norm_physical_and_spectral=True, # set standardized mean and variance for each feature
feature_weights=np.diag([.1,.1]), # should we weight the extra features (curvature) more/less than spectral
n_coords_spectral_ordering=20000, # How many points on mesh to use for ordering spectral coordinates ()
n_coords_spectral_registration=1000, # How many points to use for spectral registrtaion (usually random subsample)
initial_correspondence_type='kd', # kd = nearest neightbor, hungarian = minimum cost of assigning between graphs (more compute heavy)
final_correspondence_type='kd' # kd = nearest neightbor, hungarian = minimum cost of assigning between graphs (more compute heavy)
):
if 'pyfocusr' not in sys.modules:
raise ModuleNotFoundError('pyfocusr is not installed & is necessary for non-rigid registration.')
if final_pt_location not in ['weighted_average', 'nearest_neighbour']:
raise Exception('Did not specify appropriate final_pt_location, must be either "weighted_average", or "nearest_neighbour"')
# Test if mesh is a vtk mesh, or a pymsky.Mesh object.
if isinstance(target_mesh, vtk.vtkPolyData):
vtk_mesh_target = target_mesh
else:
try:
vtk_mesh_target = target_mesh.mesh
except:
raise Exception(f'expected type vtk.vtkPolyData or pymskt.mesh.Mesh, got: {type(target_mesh)}')
if isinstance(source_mesh, vtk.vtkPolyData):
vtk_mesh_source = source_mesh
else:
try:
vtk_mesh_source = source_mesh.mesh
except:
raise Exception(f'expected type vtk.vtkPolyData or pymskt.mesh.Mesh, got: {type(target_mesh)}')
reg = pyfocusr.Focusr(
vtk_mesh_target=vtk_mesh_target,
vtk_mesh_source=vtk_mesh_source,
icp_register_first=icp_register_first,
icp_registration_mode=icp_registration_mode,
icp_reg_target_to_source=icp_reg_target_to_source,
n_spectral_features=n_spectral_features,
n_extra_spectral=n_extra_spectral,
target_eigenmap_as_reference=target_eigenmap_as_reference,
get_weighted_spectral_coords=get_weighted_spectral_coords,
list_features_to_calc=list_features_to_calc,
use_features_as_coords=use_features_as_coords,
rigid_reg_max_iterations=rigid_reg_max_iterations,
non_rigid_alpha=non_rigid_alpha,
non_rigid_beta=non_rigid_beta,
non_rigid_n_eigens=non_rigid_n_eigens,
non_rigid_max_iterations=non_rigid_max_iterations,
rigid_before_non_rigid_reg=rigid_before_non_rigid_reg,
projection_smooth_iterations=projection_smooth_iterations,
graph_smoothing_iterations=graph_smoothing_iterations,
feature_smoothing_iterations=feature_smoothing_iterations,
include_points_as_features=include_points_as_features,
norm_physical_and_spectral=norm_physical_and_spectral,
feature_weights=feature_weights,
n_coords_spectral_ordering=n_coords_spectral_ordering,
n_coords_spectral_registration=n_coords_spectral_registration,
initial_correspondence_type=initial_correspondence_type,
final_correspondence_type=final_correspondence_type
)
reg.align_maps()
if final_pt_location == 'weighted_average':
reg.get_source_mesh_transformed_weighted_avg()
mesh_transformed_to_target = reg.weighted_avg_transformed_mesh
elif final_pt_location == 'nearest_neighbour':
reg.get_source_mesh_transformed_nearest_neighbour()
mesh_transformed_to_target = reg.nearest_neighbour_transformed_mesh
return mesh_transformed_to_target </code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
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<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="pymskt.mesh" href="index.html">pymskt.mesh</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="pymskt.mesh.meshRegistration.get_icp_transform" href="#pymskt.mesh.meshRegistration.get_icp_transform">get_icp_transform</a></code></li>
<li><code><a title="pymskt.mesh.meshRegistration.non_rigidly_register" href="#pymskt.mesh.meshRegistration.non_rigidly_register">non_rigidly_register</a></code></li>
</ul>
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