52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
import open3d as o3d
|
||
import numpy as np
|
||
from scipy.spatial import KDTree
|
||
def chamfer_distance(pcd1, pcd2):
|
||
"""
|
||
计算两个点云之间的 CD 距离（Chamfer Distance）
|
||
|
||
Args:
|
||
pcd1: Open3D 点云对象1
|
||
pcd2: Open3D 点云对象2
|
||
|
||
Returns:
|
||
cd: Chamfer Distance 值
|
||
cd1: 点云1 到 点云2 的平均距离
|
||
cd2: 点云2 到 点云1 的平均距离
|
||
"""
|
||
# 获取点坐标
|
||
points1 = np.asarray(pcd1.points)
|
||
points2 = np.asarray(pcd2.points)
|
||
|
||
# 构建 KD-Tree 加速最近邻搜索
|
||
tree1 = KDTree(points1)
|
||
tree2 = KDTree(points2)
|
||
|
||
# 计算 points1 中每个点到 points2 的最近距离
|
||
dist1, _ = tree2.query(points1, k=1)
|
||
cd1 = np.mean(dist1 ** 2) # 平方距离的平均值
|
||
|
||
# 计算 points2 中每个点到 points1 的最近距离
|
||
dist2, _ = tree1.query(points2, k=1)
|
||
cd2 = np.mean(dist2 ** 2) # 平方距离的平均值
|
||
|
||
# 总 CD 距离（双向平均）
|
||
cd = cd1 + cd2
|
||
|
||
return np.sqrt(cd), cd1, cd2
|
||
pcd = o3d.io.read_point_cloud("E:\point_distance\data\part_car_left_side.ply")
|
||
points = np.asarray(pcd.points)
|
||
colors = np.tile([0.8, 0.7, 1.0], (len(pcd.points), 1))
|
||
pcd.colors = o3d.utility.Vector3dVector(colors)
|
||
o3d.visualization.draw_geometries([pcd],window_name="1")
|
||
# 半径离群点移除
|
||
cl, ind = pcd.remove_radius_outlier(nb_points=5, radius=20)
|
||
|
||
# 提取内点
|
||
inlier_cloud = pcd.select_by_index(ind)
|
||
outlier_cloud = pcd.select_by_index(ind, invert=True)
|
||
|
||
print(f"原始点云: {len(pcd.points)} 点")
|
||
print(f"滤波后: {len(inlier_cloud.points)} 点")
|
||
cd,cd1,cd2=chamfer_distance(pcd1=pcd,pcd2=inlier_cloud)
|
||
print(cd) |