1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
|
# -*- coding: utf-8 -*-
# Spatial Partitioning and Search Operations with Octrees
# http://pointclouds.org/documentation/tutorials/octree.php#octree-search
import pcl
import numpy as np
import random
# pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
cloud = pcl.PointCloud()
##
# // Generate pointcloud data
# cloud->width = 1000;
# cloud->height = 1;
# cloud->points.resize (cloud->width * cloud->height);
#
# for (size_t i = 0; i < cloud->points.size (); ++i)
# {
# cloud->points[i].x = 1024.0f * rand () / (RAND_MAX + 1.0f);
# cloud->points[i].y = 1024.0f * rand () / (RAND_MAX + 1.0f);
# cloud->points[i].z = 1024.0f * rand () / (RAND_MAX + 1.0f);
# }
#
points = np.zeros((1000, 3), dtype=np.float32)
RAND_MAX = 1024.0
for i in range(0, 1000):
points[i][0] = 1024 * random.random () / RAND_MAX
points[i][1] = 1024 * random.random () / RAND_MAX
points[i][2] = 1024 * random.random () / RAND_MAX
cloud.from_array(points)
# pcl::octree::OctreePointCloudSearch<pcl::PointXYZ> octree (resolution);
# octree.setInputCloud (cloud);
# octree.addPointsFromInputCloud ();
# resolution = 128.0
# x,y,z Area Filter
resolution = 0.2
octree = cloud.make_octreeSearch(resolution)
octree.add_points_from_input_cloud()
# pcl::PointXYZ searchPoint;
# searchPoint.x = 1024.0f * rand () / (RAND_MAX + 1.0f);
# searchPoint.y = 1024.0f * rand () / (RAND_MAX + 1.0f);
# searchPoint.z = 1024.0f * rand () / (RAND_MAX + 1.0f);
searchPoint = pcl.PointCloud()
searchPoints = np.zeros((1, 3), dtype=np.float32)
# searchPoints[0][0] = 1024 * random.random () / (RAND_MAX + 1.0)
# searchPoints[0][1] = 1024 * random.random () / (RAND_MAX + 1.0)
# searchPoints[0][2] = 1024 * random.random () / (RAND_MAX + 1.0)
searchPoints[0][0] = 1024 * random.random () / (RAND_MAX + 1.0)
searchPoints[0][1] = 1024 * random.random () / (RAND_MAX + 1.0)
searchPoints[0][2] = 1024 * random.random () / (RAND_MAX + 1.0)
searchPoint.from_array(searchPoints)
##
# // Neighbors within voxel search
# std::vector<int> pointIdxVec;
#
# if (octree.voxelSearch (searchPoint, pointIdxVec))
# {
# std::cout << "Neighbors within voxel search at (" << searchPoint.x
# << " " << searchPoint.y
# << " " << searchPoint.z << ")"
# << std::endl;
#
# for (size_t i = 0; i < pointIdxVec.size (); ++i)
# std::cout << " " << cloud->points[pointIdxVec[i]].x
# << " " << cloud->points[pointIdxVec[i]].y
# << " " << cloud->points[pointIdxVec[i]].z << std::endl;
# }
ind = octree.VoxelSearch(searchPoint)
print ('Neighbors within voxel search at (' + str(searchPoint[0][0]) + ' ' + str(searchPoint[0][1]) + ' ' + str(searchPoint[0][2]) + ')')
# for i in range(0, ind.size):
for i in range(0, ind.size):
print ('index = ' + str(ind[i]))
print ('(' + str(cloud[ind[i]][0]) + ' ' + str(cloud[ind[i]][1]) + ' ' + str(cloud[ind[i]][2]))
##
# // K nearest neighbor search
# std::vector<int> pointIdxNKNSearch;
# std::vector<float> pointNKNSquaredDistance;
#
# std::cout << "K nearest neighbor search at (" << searchPoint.x
# << " " << searchPoint.y
# << " " << searchPoint.z
# << ") with K=" << K << std::endl;
K = 10
print ('K nearest neighbor search at (' + str(searchPoint[0][0]) + ' ' + str(searchPoint[0][1]) + ' ' + str(searchPoint[0][2]) + ') with K=' + str(K))
# if (octree.nearestKSearch (searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) > 0)
# {
# for (size_t i = 0; i < pointIdxNKNSearch.size (); ++i)
# std::cout << " " << cloud->points[ pointIdxNKNSearch[i] ].x
# << " " << cloud->points[ pointIdxNKNSearch[i] ].y
# << " " << cloud->points[ pointIdxNKNSearch[i] ].z
# << " (squared distance: " << pointNKNSquaredDistance[i] << ")" << std::endl;
# }
# // Neighbors within radius search
[ind, sqdist] = octree.nearest_k_search_for_cloud(searchPoint, K)
# if nearest_k_search_for_cloud
for i in range(0, ind.size):
print ('(' + str(cloud[ind[0][i]][0]) + ' ' + str(cloud[ind[0][i]][1]) + ' ' + str(cloud[ind[0][i]][2]) + ' (squared distance: ' + str(sqdist[0][i]) + ')')
##
# std::vector<int> pointIdxRadiusSearch;
# std::vector<float> pointRadiusSquaredDistance;
# float radius = 256.0f * rand () / (RAND_MAX + 1.0f);
# std::cout << "Neighbors within radius search at (" << searchPoint.x
# << " " << searchPoint.y
# << " " << searchPoint.z
# << ") with radius=" << radius << std::endl;
#
radius = 256.0 * random.random () / (RAND_MAX + 1.0)
print ('Neighbors within radius search at (' + str(searchPoint[0][0]) + ' ' + str(searchPoint[0][1]) + ' ' + str(searchPoint[0][2]) + ') with radius=' + str(radius))
# if (octree.radiusSearch (searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0)
# {
# for (size_t i = 0; i < pointIdxRadiusSearch.size (); ++i)
# std::cout << " " << cloud->points[ pointIdxRadiusSearch[i] ].x
# << " " << cloud->points[ pointIdxRadiusSearch[i] ].y
# << " " << cloud->points[ pointIdxRadiusSearch[i] ].z
# << " (squared distance: " << pointRadiusSquaredDistance[i] << ")" << std::endl;
# }
###
# [ind, sqdist] = octree.radius_search_for_cloud (searchPoint, radius)
# Exception ignored in: 'pcl._pcl.to_point_t'
# [ind, sqdist] = octree.radius_search (searchPoint, radius, 10)
searchPoints = (searchPoint[0][0], searchPoint[0][1], searchPoint[0][2])
[ind, sqdist] = octree.radius_search (searchPoints, radius, 10)
# Function radius_search
for i in range(0, ind.size):
print ('(' + str(cloud[ind[i]][0]) + ' ' + str(cloud[ind[i]][1]) + ' ' + str(cloud[ind[i]][2]) + ' (squared distance: ' + str(sqdist[i]) + ')')
# Function radius_search_for_cloud
# for i in range(0, ind.size):
# print ('(' + str(cloud[ind[0][i]][0]) + ' ' + str(cloud[ind[0][i]][1]) + ' ' + str(cloud[ind[0][i]][2]) + ' (squared distance: ' + str(sqdist[0][i]) + ')')
|
