lipan
29f1d45d66
算法更新: delivery v1.2.1、平整度v1.5、测距v2.2、整车配准v1.3 适配器: vehicle-registration 新增、各算法 manifest 和 adapter 更新 后端: taskCadFiles/source 字段迁移、derivedPointClouds 注册、取消任务联动 前端: ElapsedTime 时长显示、两步算法流程、canRun 逻辑修复 文档: 新增5份算法性能分析报告
40 lines
1.6 KiB
Python
40 lines
1.6 KiB
Python
from __future__ import annotations
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import copy
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import numpy as np
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import open3d as o3d
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class CurvatureEstimator:
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"""Estimate local point-cloud curvature using neighborhood covariance."""
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def compute(self, pointcloud: o3d.geometry.PointCloud, radius: float = 2.0, max_nn: int = 30) -> np.ndarray:
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pointcloud.estimate_normals(o3d.geometry.KDTreeSearchParamHybrid(radius=radius, max_nn=max_nn))
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points = np.asarray(pointcloud.points)
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kdtree = o3d.geometry.KDTreeFlann(pointcloud)
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curvatures = np.zeros(len(points), dtype=np.float64)
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for i, point in enumerate(points):
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_, indices, _ = kdtree.search_hybrid_vector_3d(point, radius, max_nn)
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if len(indices) < 3:
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curvatures[i] = 1.0
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continue
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neighbors = points[indices]
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cov = np.cov((neighbors - neighbors.mean(axis=0)).T)
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eigenvalues = np.linalg.eigvalsh(cov)
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denom = eigenvalues.sum()
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curvatures[i] = float(eigenvalues[0] / denom) if denom > 0 else 0.0
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return curvatures
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def colorize(self, pointcloud: o3d.geometry.PointCloud, curvatures: np.ndarray, color_scale: float = 3.0) -> o3d.geometry.PointCloud:
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pcd = copy.deepcopy(pointcloud)
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normalized = curvatures / (np.max(curvatures) + 1e-12)
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normalized = np.clip(normalized * color_scale, 0, 1)
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colors = np.zeros((len(normalized), 3))
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colors[:, 0] = normalized
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colors[:, 1] = 1 - normalized
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pcd.colors = o3d.utility.Vector3dVector(colors)
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return pcd
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