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网站seo完整seo优化方案,腾讯 云上做网站教程,佛山专业网站建设公司哪家好,东莞免费建网站企业一#xff1a;主要的知识点 1、说明 本文只是教程内容的一小段#xff0c;因博客字数限制#xff0c;故进行拆分。主教程链接#xff1a;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#xff0c;②配准结果的检测…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()