做网站背景全覆盖的代码,为什么做动漫短视频网站,深圳公租房官网,网站建设的电话回访使用 NearestNeighbors 进行点云分析
在数据分析和机器学习领域#xff0c;最近邻算法#xff08;Nearest Neighbors#xff09;是一种常用的非参数方法。它广泛应用于分类、回归和聚类分析等任务。下面将介绍如何使用 scikit-learn 库中的 NearestNeighbors 类来进行点云数…使用 NearestNeighbors 进行点云分析
在数据分析和机器学习领域最近邻算法Nearest Neighbors是一种常用的非参数方法。它广泛应用于分类、回归和聚类分析等任务。下面将介绍如何使用 scikit-learn 库中的 NearestNeighbors 类来进行点云数据的处理并通过 Open3D 库进行可视化展示。 最近邻算法简介
最近邻算法是一种基于距离的算法它通过计算数据点之间的距离来查找给定数据点的最近邻居。常用的距离度量包括欧氏距离、曼哈顿距离和余弦相似度等。最近邻算法的优点在于简单易懂且无需假设数据的分布形式适用于各种类型的数据。
代码示例
使用 NearestNeighbors 查找点云数据的最近邻并使用 Open3D 进行可视化。 步骤一导入必要的库
import open3d as o3d
import numpy as np
from sklearn.neighbors import NearestNeighbors
import time步骤二定义函数来创建点与点之间的连接线
def create_lines_from_points(points, k_neighbors6, color[0, 1, 0]):if len(points) 2:return Nonestart_time time.time()neighbors NearestNeighbors(n_neighborsk_neighbors)neighbors.fit(points)distances, indices neighbors.kneighbors(points)end_time time.time()print(fNearest neighbors computation time: {end_time - start_time:.4f} seconds)start_time time.time()lines []for i in range(len(points)):for j in indices[i]:if i j: # 避免重复的线lines.append([i, j])end_time time.time()print(fLine creation time: {end_time - start_time:.4f} seconds)colors [color for i in range(len(lines))]line_set o3d.geometry.LineSet()line_set.points o3d.utility.Vector3dVector(points)line_set.lines o3d.utility.Vector2iVector(lines)line_set.colors o3d.utility.Vector3dVector(colors)return line_set步骤三加载点云数据
使用点云数据文件 .pcd 的内容。
pcd_file \
VERSION 0.7
FIELDS x y z
SIZE 4 4 4
TYPE F F F
COUNT 1 1 1
WIDTH 28
HEIGHT 1
VIEWPOINT 0 0 0 1 0 0 0
POINTS 28
DATA ascii
0.301945 -0.1810271 1.407832
0.3025161 -0.1733161 1.322455
0.3003909 -0.167791 1.717239
0.2926154 -0.1333728 1.246899
0.2981626 -0.1311488 1.376031
0.300947 -0.1268353 1.719725
0.2944916 -0.1170874 1.545582
0.3008177 -0.09701672 1.395218
0.2989618 -0.08497152 1.699149
0.3039065 -0.07092351 1.32867
0.3031552 -0.05290076 1.509094
0.2906472 0.02252534 1.617192
0.2972519 0.02116165 1.457043
0.3024158 0.02067187 1.402361
0.2987708 0.01975626 1.286629
0.3014581 0.06462696 1.304869
0.289153 0.1107126 1.859879
0.2879259 0.1625713 1.583842
0.2952633 0.1989845 1.431798
0.3078183 -0.1622952 1.816048
0.3001072 -0.147239 1.970708
0.2990342 -0.1194922 1.950798
0.2979593 -0.09225944 1.931052
0.2929263 0.02492997 1.965327
0.3061717 0.1117098 1.621875
0.3004842 0.03407142 1.999085
0.3023082 -0.1527775 1.553968
0.3008434 0.250506 1.55337
# 解析点云数据
lines pcd_file.strip().split(\n)
points []
for line in lines[11:]:points.append([float(value) for value in line.split()])
points np.array(points)步骤四创建连接线并进行可视化
# 创建连接线并进行可视化
line_set create_lines_from_points(points, k_neighbors6, color[0, 1, 0])
o3d.visualization.draw_geometries([line_set])结论
以上展示了如何使用 scikit-learn 中的 NearestNeighbors 类来计算点云数据的最近邻并使用 Open3D 库将结果进行可视化。这种方法可以用于点云数据的分析、物体检测以及3D建模等多个领域。
完整代码
import open3d as o3d
import numpy as np
from sklearn.neighbors import NearestNeighbors
import timedef create_lines_from_points(points, k_neighbors6, color[0, 1, 0]):if len(points) 2:return Nonestart_time time.time()neighbors NearestNeighbors(n_neighborsk_neighbors)neighbors.fit(points)distances, indices neighbors.kneighbors(points)end_time time.time()print(fNearest neighbors computation time: {end_time - start_time:.4f} seconds)start_time time.time()lines []for i in range(len(points)):for j in indices[i]:if i j: # avoid duplicate lineslines.append([i, j])end_time time.time()print(fLine creation time: {end_time - start_time:.4f} seconds)colors [color for i in range(len(lines))]line_set o3d.geometry.LineSet()line_set.points o3d.utility.Vector3dVector(points)line_set.lines o3d.utility.Vector2iVector(lines)line_set.colors o3d.utility.Vector3dVector(colors)return line_set# Load point cloud data from a .pcd file
pcd_file \
VERSION 0.7
FIELDS x y z
SIZE 4 4 4
TYPE F F F
COUNT 1 1 1
WIDTH 28
HEIGHT 1
VIEWPOINT 0 0 0 1 0 0 0
POINTS 28
DATA ascii
0.301945 -0.1810271 1.407832
0.3025161 -0.1733161 1.322455
0.3003909 -0.167791 1.717239
0.2926154 -0.1333728 1.246899
0.2981626 -0.1311488 1.376031
0.300947 -0.1268353 1.719725
0.2944916 -0.1170874 1.545582
0.3008177 -0.09701672 1.395218
0.2989618 -0.08497152 1.699149
0.3039065 -0.07092351 1.32867
0.3031552 -0.05290076 1.509094
0.2906472 0.02252534 1.617192
0.2972519 0.02116165 1.457043
0.3024158 0.02067187 1.402361
0.2987708 0.01975626 1.286629
0.3014581 0.06462696 1.304869
0.289153 0.1107126 1.859879
0.2879259 0.1625713 1.583842
0.2952633 0.1989845 1.431798
0.3078183 -0.1622952 1.816048
0.3001072 -0.147239 1.970708
0.2990342 -0.1194922 1.950798
0.2979593 -0.09225944 1.931052
0.2929263 0.02492997 1.965327
0.3061717 0.1117098 1.621875
0.3004842 0.03407142 1.999085
0.3023082 -0.1527775 1.553968
0.3008434 0.250506 1.55337
# Parse the point cloud data
lines pcd_file.strip().split(\n)
points []
for line in lines[11:]:points.append([float(value) for value in line.split()])
points np.array(points)# Create lines from points and visualize
line_set create_lines_from_points(points, k_neighbors6, color[0, 1, 0])
o3d.visualization.draw_geometries([line_set])
