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
60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
import open3d as o3d
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import numpy as np
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from scipy.spatial import KDTree
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from plyfile import PlyData
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def chamfer_distance(pcd1, pcd2):
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"""
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计算两个点云之间的 CD 距离（Chamfer Distance）
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Args:
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pcd1: Open3D 点云对象1
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pcd2: Open3D 点云对象2
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Returns:
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cd: Chamfer Distance 值
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cd1: 点云1 到 点云2 的平均距离
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cd2: 点云2 到 点云1 的平均距离
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"""
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# 获取点坐标
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points1 = np.asarray(pcd1.points)
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points2 = np.asarray(pcd2.points)
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# 构建 KD-Tree 加速最近邻搜索
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tree1 = KDTree(points1)
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tree2 = KDTree(points2)
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# 计算 points1 中每个点到 points2 的最近距离
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dist1, _ = tree2.query(points1, k=1)
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cd1 = np.mean(dist1 ** 2) # 平方距离的平均值
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# 计算 points2 中每个点到 points1 的最近距离
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dist2, _ = tree1.query(points2, k=1)
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cd2 = np.mean(dist2 ** 2) # 平方距离的平均值
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# 总 CD 距离（双向平均）
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cd = cd1 + cd2
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return cd, cd1, cd2
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pcd = o3d.io.read_point_cloud("E:\point_distance\data\part_car_head.ply")
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colors = np.tile([1, 0, 0], (len(pcd.points), 1))
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pcd.colors = o3d.utility.Vector3dVector(colors)
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# ply_data = PlyData.read(r"E:\point_distance\data\part_car_head.ply")
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# colors=np.asarray(ply_data.colors)
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# with open("E:\point_distance\data\part_car_head.ply", 'r',encoding="utf-8") as f:
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# lines = f.readlines()
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# for line in lines[:30]: # 打印头部
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# print(line.strip())
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# o3d.visualization.draw_geometries([pcd])
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# points = np.asarray(pcd.points)
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# colors = np.asarray(pcd.colors)
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# normals = np.asarray(pcd.normals)
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cl, ind = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=50.0)
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inlier_cloud = pcd.select_by_index(ind)
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colors=np.tile([0,1,0],(len(inlier_cloud.points),1))
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inlier_cloud.colors=o3d.utility.Vector3dVector(colors)
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o3d.visualization.draw_geometries([inlier_cloud])
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o3d.visualization.draw_geometries([pcd])
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o3d.visualization.draw_geometries()
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cd,cd1,cd2=chamfer_distance(pcd1=pcd,pcd2=inlier_cloud)
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print(cd) |