一：主要的知识点
1、说明
本文只是教程内容的一小段，因博客字数限制，故进行拆分。主教程链接：vtk教程——逐行解析官网所有Python示例-CSDN博客
2、知识点纪要
本段代码主要涉及的有①ICP模型配准，②配准结果的检测，③OBB包围盒
二：代码及注释
#!/usr/bin/env python # -*- coding: utf-8 -*- import math from pathlib import Path # noinspection PyUnresolvedReferences import vtkmodules.vtkInteractionStyle # noinspection PyUnresolvedReferences import vtkmodules.vtkRenderingOpenGL2 from vtkmodules.vtkCommonColor import vtkNamedColors from vtkmodules.vtkCommonCore import ( VTK_DOUBLE_MAX, vtkPoints ) from vtkmodules.vtkCommonCore import ( VTK_VERSION_NUMBER, vtkVersion ) from vtkmodules.vtkCommonDataModel import ( vtkIterativeClosestPointTransform, vtkPolyData ) from vtkmodules.vtkCommonTransforms import ( vtkLandmarkTransform, vtkTransform ) from vtkmodules.vtkFiltersGeneral import ( vtkOBBTree, vtkTransformPolyDataFilter ) from vtkmodules.vtkFiltersModeling import vtkHausdorffDistancePointSetFilter from vtkmodules.vtkIOGeometry import ( vtkBYUReader, vtkOBJReader, vtkSTLReader ) from vtkmodules.vtkIOLegacy import ( vtkPolyDataReader, vtkPolyDataWriter ) from vtkmodules.vtkIOPLY import vtkPLYReader from vtkmodules.vtkIOXML import vtkXMLPolyDataReader from vtkmodules.vtkInteractionWidgets import ( vtkCameraOrientationWidget, vtkOrientationMarkerWidget ) from vtkmodules.vtkRenderingAnnotation import vtkAxesActor from vtkmodules.vtkRenderingCore import ( vtkActor, vtkDataSetMapper, vtkRenderWindow, vtkRenderWindowInteractor, vtkRenderer ) def main(): colors = vtkNamedColors() src_fn = "Data/thingiverse/Grey_Nurse_Shark.stl" tgt_fn = "Data/greatWhite.stl" print('Loading source:', src_fn) source_polydata = read_poly_data(src_fn) # Save the source polydata in case the alignment process does not improve # segmentation. original_source_polydata = vtkPolyData() original_source_polydata.DeepCopy(source_polydata) print('Loading target:', tgt_fn) target_polydata = read_poly_data(tgt_fn) # If the target orientation is markedly different, you may need to apply a # transform to orient the target with the source. # For example, when using Grey_Nurse_Shark.stl as the source and # greatWhite.stl as the target, you need to transform the target. trnf = vtkTransform() if Path(src_fn).name == 'Grey_Nurse_Shark.stl' and Path(tgt_fn).name == 'greatWhite.stl': trnf.RotateY(90) tpd = vtkTransformPolyDataFilter() tpd.SetTransform(trnf) tpd.SetInputData(target_polydata) tpd.Update() renderer = vtkRenderer() render_window = vtkRenderWindow() render_window.AddRenderer(renderer) interactor = vtkRenderWindowInteractor() interactor.SetRenderWindow(render_window) """ 对集合 A 中的每个点 a，找到 集合 B 中离它最近的点 b，记录这个最近距离。然后取 这些最近距离中的最大值。 对集合 B 中的每个点 b，同样在 A 中找到最近的点 a，取最大值。 两者的最大值，就是 Hausdorff 距离 """ distance = vtkHausdorffDistancePointSetFilter() distance.SetInputData(0, tpd.GetOutput()) distance.SetInputData(1, source_polydata) distance.Update() """ 计算Hausdorff距离后，内部会算出最大Hausdorff距离，各点的距离分布，统计信息存在FieldData中 GetFieldData() 取场数据（FieldData，用来存全局统计结果） GetArray('HausdorffDistance') 获取名字叫 HausdorffDistance 的数组 这个数组通常只有一个值：计算出的 Hausdorff 距离 """ distance_before_align = distance.GetOutput(0).GetFieldData().GetArray('HausdorffDistance').GetComponent(0, 0) # Get initial alignment using oriented bounding boxes. align_bounding_boxes(source_polydata, tpd.GetOutput()) distance.SetInputData(0, tpd.GetOutput()) distance.SetInputData(1, source_polydata) distance.Modified() distance.Update() distance_after_align = distance.GetOutput(0).GetFieldData().GetArray('HausdorffDistance').GetComponent(0, 0) best_distance = min(distance_before_align, distance_after_align) if distance_after_align > distance_before_align: source_polydata.DeepCopy(original_source_polydata) # Refine the alignment using IterativeClosestPoint. icp = vtkIterativeClosestPointTransform() icp.SetSource(source_polydata) icp.SetTarget(tpd.GetOutput()) """ SetModeToRigidBody 将你内部用来计算点对齐的变换模式设置为刚体变换 """ icp.GetLandmarkTransform().SetModeToRigidBody() icp.SetMaximumNumberOfLandmarks(100) """ SetMaximumMeanDistance 设置平均距离的收敛值。 如果源点与目标点之间的平均距离小于这个值，算法就会认为已经收敛，并提前停止迭代 """ icp.SetMaximumMeanDistance(.00001) icp.SetMaximumNumberOfIterations(500) """ CheckMeanDistanceOn 启用平均距离检查。 ICP 在每次迭代后都检查是否达到了上面设置的 MaximumMeanDistance 阈值 """ icp.CheckMeanDistanceOn() """ StartByMatchingCentroidsOn 启用质心匹配作为初始步骤 在开始迭代之前，ICP 会首先计算源模型和目标模型的质心，并将源模型的质心平移到目标模型的质心位置 """ icp.StartByMatchingCentroidsOn() icp.Update() icp_mean_distance = icp.GetMeanDistance() # print(icp) lm_transform = icp.GetLandmarkTransform() transform = vtkTransformPolyDataFilter() transform.SetInputData(source_polydata) """ SetTransform(icp) vtkIterativeClosestPointTransform 继承自 vtkTransform 它可以直接作为 SetTransform 的参数。这告诉过滤器：“请将ICP 算法最终计算出的所有变换（包括旋转、平移等）应用到源模型上 """ transform.SetTransform(lm_transform) transform.SetTransform(icp) transform.Update() distance.SetInputData(0, tpd.GetOutput()) distance.SetInputData(1, transform.GetOutput()) distance.Update() # Note: If there is an error extracting eigenfunctions, then this will be zero. distance_after_icp = distance.GetOutput(0).GetFieldData().GetArray('HausdorffDistance').GetComponent(0, 0) # Check if ICP worked. if not (math.isnan(icp_mean_distance) or math.isinf(icp_mean_distance)): if distance_after_icp < best_distance: best_distance = distance_after_icp print('Distances:') print(' Before aligning: {:0.5f}'.format(distance_before_align)) print(' Aligning using oriented bounding boxes: {:0.5f}'.format(distance_before_align)) print(' Aligning using IterativeClosestPoint: {:0.5f}'.format(distance_after_icp)) print(' Best distance: {:0.5f}'.format(best_distance)) # Select the source to use. source_mapper = vtkDataSetMapper() if best_distance == distance_before_align: source_mapper.SetInputData(original_source_polydata) print('Using original alignment') elif best_distance == distance_after_align: source_mapper.SetInputData(source_polydata) print('Using alignment by OBB') else: source_mapper.SetInputConnection(transform.GetOutputPort()) print('Using alignment by ICP') source_mapper.ScalarVisibilityOff() writer = vtkPolyDataWriter() if best_distance == distance_before_align: writer.SetInputData(original_source_polydata) elif best_distance == distance_after_align: writer.SetInputData(source_polydata) else: writer.SetInputData(transform.GetOutput()) writer.SetFileName('AlignedSource.vtk') writer.Write() writer.SetInputData(tpd.GetOutput()) writer.SetFileName('Target.vtk') writer.Write() source_actor = vtkActor() source_actor.SetMapper(source_mapper) source_actor.GetProperty().SetOpacity(0.6) source_actor.GetProperty().SetDiffuseColor( colors.GetColor3d('White')) renderer.AddActor(source_actor) target_mapper = vtkDataSetMapper() target_mapper.SetInputData(tpd.GetOutput()) target_mapper.ScalarVisibilityOff() target_actor = vtkActor() target_actor.SetMapper(target_mapper) target_actor.GetProperty().SetDiffuseColor( colors.GetColor3d('Tomato')) renderer.AddActor(target_actor) render_window.AddRenderer(renderer) renderer.SetBackground(colors.GetColor3d("sea_green_light")) renderer.UseHiddenLineRemovalOn() if vtk_version_ok(9, 0, 20210718): try: cam_orient_manipulator = vtkCameraOrientationWidget() cam_orient_manipulator.SetParentRenderer(renderer) # Enable the widget. cam_orient_manipulator.On() except AttributeError: pass else: axes = vtkAxesActor() widget = vtkOrientationMarkerWidget() rgba = [0.0, 0.0, 0.0, 0.0] colors.GetColor("Carrot", rgba) widget.SetOutlineColor(rgba[0], rgba[1], rgba[2]) widget.SetOrientationMarker(axes) widget.SetInteractor(interactor) widget.SetViewport(0.0, 0.0, 0.2, 0.2) widget.EnabledOn() widget.InteractiveOn() render_window.SetSize(640, 480) render_window.Render() render_window.SetWindowName('AlignTwoPolyDatas') interactor.Start() def vtk_version_ok(major, minor, build): """ Check the VTK version. :param major: Major version. :param minor: Minor version. :param build: Build version. :return: True if the requested VTK version is greater or equal to the actual VTK version. """ needed_version = 10000000000 * int(major) + 100000000 * int(minor) + int(build) try: vtk_version_number = VTK_VERSION_NUMBER except AttributeError: # as error: ver = vtkVersion() vtk_version_number = 10000000000 * ver.GetVTKMajorVersion() + 100000000 * ver.GetVTKMinorVersion() \ + ver.GetVTKBuildVersion() if vtk_version_number >= needed_version: return True else: return False def read_poly_data(file_name): import os path, extension = os.path.splitext(file_name) extension = extension.lower() if extension == ".ply": reader = vtkPLYReader() reader.SetFileName(file_name) reader.Update() poly_data = reader.GetOutput() elif extension == ".vtp": reader = vtkXMLPolyDataReader() reader.SetFileName(file_name) reader.Update() poly_data = reader.GetOutput() elif extension == ".obj": reader = vtkOBJReader() reader.SetFileName(file_name) reader.Update() poly_data = reader.GetOutput() elif extension == ".stl": reader = vtkSTLReader() reader.SetFileName(file_name) reader.Update() poly_data = reader.GetOutput() elif extension == ".vtk": reader = vtkPolyDataReader() reader.SetFileName(file_name) reader.Update() poly_data = reader.GetOutput() elif extension == ".g": reader = vtkBYUReader() reader.SetGeometryFileName(file_name) reader.Update() poly_data = reader.GetOutput() else: # Return a None if the extension is unknown. poly_data = None return poly_data def align_bounding_boxes(source, target): # Use OBBTree to create an oriented bounding box for target and source """ vtkOBBtree tkOBBTree 是一种 空间加速结构,它会递归地把几何体拆分成 包围盒（OBB）层次结构 MaxLevel 控制这个树的 最大递归深度 这里设置成1，意思是： 只构建 根节点 + 一层子节点 的 OBB 树 层数越小 → 树越“粗糙”，速度快但精度低 层数越大 → 树越“细致”，适合做精确的碰撞检测或最近点查询 """ source_obb_tree = vtkOBBTree() source_obb_tree.SetDataSet(source) source_obb_tree.SetMaxLevel(1) source_obb_tree.BuildLocator() target_obb_tree = vtkOBBTree() target_obb_tree.SetDataSet(target) target_obb_tree.SetMaxLevel(1) target_obb_tree.BuildLocator() """ BuildLocator 真正构建 OBB 树的层次结构 在设置了数据集（SetDataSet）和参数（SetMaxLevel）之后，你必须调用它来“编译”出树， 才能进行后续查询（比如射线相交、碰撞检测、最近点搜索） """ source_landmarks = vtkPolyData() source_obb_tree.GenerateRepresentation(0, source_landmarks) """ GenerateRepresentation 生成包围盒的几何形状 0：这个参数指定了要生成的表示层级。vtkOBBTree 是一种分层的包围盒，0 表示最高层级，也就是整个模型的最外层包围盒 source_landmarks:GenerateRepresentation 方法会把生成的8个顶点和12条边（一个六面体）的几何数据填充到 source_landmarks 这个 vtkPolyData 对象中 """ target_landmarks = vtkPolyData() target_obb_tree.GenerateRepresentation(0, target_landmarks) """ vtkLandmarkTransform 是 VTK 里一个 点集配准（registration） 的基础类， 它根据两组 对应点（landmarks） 来计算一个最佳的变换矩阵（旋转、平移、可选缩放） 它需要两组对应点：源点集（Source Landmarks）和 目标点集（Target Landmarks） """ lm_transform = vtkLandmarkTransform() lm_transform.SetModeToSimilarity() """ LandmarkTransform 支持 3 种模式 RigidBody（刚体变换） 只允许旋转 + 平移（保持形状和大小完全不变） Similarity（相似变换） 允许旋转 + 平移 + 缩放（各向同性缩放，保持比例） Affine（仿射变换） 允许旋转 + 平移 + 缩放 + 剪切（最灵活，但可能改变形状） 这里用 Similarity 模式，说明希望计算的变换能 匹配位置和大小，但不允许形状变形 """ lm_transform.SetTargetLandmarks(target_landmarks.GetPoints()) best_distance = VTK_DOUBLE_MAX best_points = vtkPoints() best_distance = best_bounding_box( "X", target, source, target_landmarks, source_landmarks, best_distance, best_points) best_distance = best_bounding_box( "Y", target, source, target_landmarks, source_landmarks, best_distance, best_points) best_distance = best_bounding_box( "Z", target, source, target_landmarks, source_landmarks, best_distance, best_points) lm_transform.SetSourceLandmarks(best_points) lm_transform.Modified() lm_transform_pd = vtkTransformPolyDataFilter() lm_transform_pd.SetInputData(source) lm_transform_pd.SetTransform(lm_transform) lm_transform_pd.Update() source.DeepCopy(lm_transform_pd.GetOutput()) return def best_bounding_box(axis, target, source, target_landmarks, source_landmarks, best_distance, best_points): """ 这个函数的目的在于在粗略对齐阶段， 通过系统性地测试不同旋转角度，来找到源模型和目标模型之间最佳的初始对齐 """ distance = vtkHausdorffDistancePointSetFilter() test_transform = vtkTransform() """ vtkTransformPolyDataFilter 是将一个 vtkPolyData 数据集应用几何变换，然后生成一个新的、已变换的 vtkPolyData 对象 """ test_transform_pd = vtkTransformPolyDataFilter() """ vtkLandmarkTransform 用于两个点集的配准 是vtkTransform的子类，可以根据源点集和目标点击来计算一个最佳的变换矩阵 """ lm_transform = vtkLandmarkTransform() lm_transform.SetModeToSimilarity() lm_transform.SetTargetLandmarks(target_landmarks.GetPoints()) lm_transform_pd = vtkTransformPolyDataFilter() source_center = source_landmarks.GetCenter() delta = 90.0 for i in range(0, 4): angle = delta * i # Rotate about center test_transform.Identity() test_transform.Translate(source_center[0], source_center[1], source_center[2]) if axis == "X": test_transform.RotateX(angle) elif axis == "Y": test_transform.RotateY(angle) else: test_transform.RotateZ(angle) test_transform.Translate(-source_center[0], -source_center[1], -source_center[2]) test_transform_pd.SetTransform(test_transform) test_transform_pd.SetInputData(source_landmarks) test_transform_pd.Update() lm_transform.SetSourceLandmarks(test_transform_pd.GetOutput().GetPoints()) lm_transform.Modified() lm_transform_pd.SetInputData(source) lm_transform_pd.SetTransform(lm_transform) lm_transform_pd.Update() distance.SetInputData(0, target) distance.SetInputData(1, lm_transform_pd.GetOutput()) distance.Update() test_distance = distance.GetOutput(0).GetFieldData().GetArray("HausdorffDistance").GetComponent(0, 0) if test_distance < best_distance: best_distance = test_distance best_points.DeepCopy(test_transform_pd.GetOutput().GetPoints()) return best_distance if __name__ == '__main__': main()
