Datasets
Models
Diff of
/docs/mesh/meshRegistration.html
[000000]
..
[9173ee]
Switch to side-by-side view
--- a +++ b/docs/mesh/meshRegistration.html @@ -0,0 +1,394 @@ +<!doctype html> +<html lang="en"> +<head> +<meta charset="utf-8"> +<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> +<meta name="generator" content="pdoc 0.10.0" /> +<title>pymskt.mesh.meshRegistration API documentation</title> +<meta name="description" content="" /> +<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin> +<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin> +<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin> +<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style> +<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style> +<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style> +<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script> +<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script> +</head> +<body> +<main> +<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> +<nav id="sidebar"> +<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> +</li> +</ul> +</nav> +</main> +<footer id="footer"> +<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p> +</footer> +</body> +</html> \ No newline at end of file