python地面点滤波 python实现CSF地面点滤波算法原理解析

import laspy
#============读取las格式的点云===========
inFile = laspy.file.File(r"40m1.las", mode='r') # 读取点云
print('X,Y,Z',inFile.x,inFile.y,inFile.z) # 输出点云坐标
print('点云个数:',len(inFile)) #读取点云个数
#============保存点云为las文件===========
h = inFile.header
outFile = laspy.file.File('666.las', mode = "w", header=h)
points = inFile #对点云进行的相关操作
outFile.points = points
outFile.close() #关闭文件完成保存

import laspy
import CSF
import numpy as np
import open3d as o3d
#============读取las文件=============
inFile = laspy.file.File(r"40m1.las", mode='r') # read a las file
points = inFile.points
xyz = np.vstack((inFile.x, inFile.y, inFile.z)).transpose() # extract x, y, z and put into a list
#============布料模拟滤波============
csf = CSF.CSF()
# 参数设置
csf.params.bSloopSmooth = False    #粒子设置为不可移动
csf.params.cloth_resolution = 0.1  #布料网格分辨率
csf.params.rigidness = 3  #布料刚性参数
csf.params.time_step = 0.65
csf.params.class_threshold = 0.03 #点云与布料模拟点的距离阈值
csf.params.interations = 500      #最大迭代次数
# more details about parameter: http://ramm.bnu.edu.cn/projects/CSF/download/
csf.setPointCloud(xyz)
ground = CSF.VecInt()  # 地面点索引列表
non_ground = CSF.VecInt() # 非地面点索引列表
csf.do_filtering(ground, non_ground) # 执行滤波
#============保存为las文件==========
outFile = laspy.file.File(r"non_ground.las",
                          mode='w', header=inFile.header)
outFile.points = points[non_ground] # 提取非地面点保存到las
outFile.close() # 关闭文件夹

a=xyz[ground]
b=xyz[non_ground]
#=============可视化===============
def view_cloud(a, b):
    pcd = o3d.geometry.PointCloud()
    # =====numpy转point=======
    pcd.points = o3d.utility.Vector3dVector(a)

    pcd1 = o3d.geometry.PointCloud()

    pcd1.points = o3d.utility.Vector3dVector(b)
    #=======自定义颜色========
    pcd.paint_uniform_color([0, 1, 0])
    pcd1.paint_uniform_color([1, 0, 0])
    o3d.visualization.draw_geometries([pcd, pcd1],window_name='提取结果')
    o3d.visualization.draw_geometries([pcd1],window_name='非地面点')
    o3d.visualization.draw_geometries([pcd],window_name='地面点')
view_cloud(a,b)

import open3d as o3d
import CSF
import numpy as np

pc = o3d.io.read_point_cloud("数据//100m1.pcd")
xyz = np.asarray(pc.points)
csf = CSF.CSF()
# prameter settings
csf.params.bSloopSmooth = False
csf.params.cloth_resolution = 0.1
csf.params.rigidness = 3
csf.params.time_step = 0.65
csf.params.class_threshold = 0.03
csf.params.interations = 500
# more details about parameter: http://ramm.bnu.edu.cn/projects/CSF/download/
csf.setPointCloud(xyz)
ground = CSF.VecInt()  # a list to indicate the index of ground points after calculation
non_ground = CSF.VecInt() # a list to indicate the index of non-ground points after calculation
csf.do_filtering(ground, non_ground) # do actual filtering.

# o3d.io.write_point_cloud("trans_of_source.pcd", non_ground)#保存点云
a=xyz[ground]
b=xyz[non_ground]
def view_cloud(a, b):
    pcd = o3d.geometry.PointCloud()
    # From numpy to Open3D
    pcd.points = o3d.utility.Vector3dVector(a)

    pcd1 = o3d.geometry.PointCloud()
    # From numpy to Open3D
    pcd1.points = o3d.utility.Vector3dVector(b)

    pcd.paint_uniform_color([0, 1, 0])
    pcd1.paint_uniform_color([1, 0, 0])
    o3d.visualization.draw_geometries([pcd, pcd1],window_name='提取结果')
    o3d.visualization.draw_geometries([pcd1],window_name='非地面点')
    o3d.visualization.draw_geometries([pcd],window_name='地面点')
view_cloud(a,b)