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
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
|
/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2010-2011, Willow Garage, Inc.
*
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the copyright holder(s) nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
*
*/
#include <iostream>
#include <pcl/test/gtest.h>
#include <pcl/common/io.h> // for copyPointCloud
#include <pcl/common/time.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/common/distances.h>
#include <pcl/search/kdtree.h> // for KdTree
using namespace pcl;
PointCloud<PointXYZ> cloud, cloud_big;
void
init ()
{
float resolution = 0.1f;
for (float z = -0.5f; z <= 0.5f; z += resolution)
for (float y = -0.5f; y <= 0.5f; y += resolution)
for (float x = -0.5f; x <= 0.5f; x += resolution)
cloud.emplace_back(x, y, z);
cloud.width = cloud.size ();
cloud.height = 1;
srand (static_cast<unsigned int> (time (nullptr)));
cloud_big.width = 640;
cloud_big.height = 480;
// Randomly create a new point cloud, use points.emplace_back
for (std::size_t i = 0; i < cloud_big.width * cloud_big.height; ++i)
cloud_big.points.emplace_back(static_cast<float>(1024 * rand() / (RAND_MAX + 1.0)),
static_cast<float>(1024 * rand() / (RAND_MAX + 1.0)),
static_cast<float>(1024 * rand() / (RAND_MAX + 1.0)));
}
/* Test for KdTree nearestKSearch */TEST (PCL, KdTree_nearestKSearch)
{
pcl::search::KdTree<PointXYZ> kdtree;
kdtree.setInputCloud (cloud.makeShared ());
PointXYZ test_point (0.01f, 0.01f, 0.01f);
unsigned int no_of_neighbors = 20;
std::multimap<float, int> sorted_brute_force_result;
for (std::size_t i = 0; i < cloud.size (); ++i)
{
float distance = euclideanDistance (cloud[i], test_point);
sorted_brute_force_result.insert (std::make_pair (distance, static_cast<int> (i)));
}
float max_dist = 0.0f;
unsigned int counter = 0;
for (auto it = sorted_brute_force_result.begin (); it != sorted_brute_force_result.end ()
&& counter < no_of_neighbors; ++it)
{
max_dist = std::max (max_dist, it->first);
++counter;
}
pcl::Indices k_indices;
k_indices.resize (no_of_neighbors);
std::vector<float> k_distances;
k_distances.resize (no_of_neighbors);
kdtree.nearestKSearch (test_point, no_of_neighbors, k_indices, k_distances);
//if (k_indices.size () != no_of_neighbors) std::cerr << "Found "<<k_indices.size ()<<" instead of "<<no_of_neighbors<<" neighbors.\n";
EXPECT_EQ (k_indices.size (), no_of_neighbors);
// Check if all found neighbors have distance smaller than max_dist
for (const auto &k_index : k_indices)
{
const PointXYZ& point = cloud[k_index];
bool ok = euclideanDistance (test_point, point) <= max_dist;
if (!ok)
ok = (std::abs (euclideanDistance (test_point, point)) - max_dist) <= 1e-6;
//if (!ok) std::cerr << k_indices[i] << " is not correct...\n";
//else std::cerr << k_indices[i] << " is correct...\n";
EXPECT_TRUE (ok);
}
ScopeTime scopeTime ("FLANN nearestKSearch");
{
pcl::search::KdTree<PointXYZ> kdtree;
//kdtree.initSearchDS ();
kdtree.setInputCloud (cloud_big.makeShared ());
for (const auto &point : cloud_big.points)
kdtree.nearestKSearch (point, no_of_neighbors, k_indices, k_distances);
}
}
/* Test the templated NN search (for different query point types) */
TEST (PCL, KdTree_differentPointT)
{
unsigned int no_of_neighbors = 20;
pcl::search::KdTree<PointXYZ> kdtree;
//kdtree.initSearchDS ();
kdtree.setInputCloud (cloud_big.makeShared ());
PointCloud<PointXYZRGB> cloud_rgb;
copyPointCloud (cloud_big, cloud_rgb);
std::vector< std::vector< float > > dists;
std::vector< pcl::Indices > indices;
kdtree.nearestKSearchT (cloud_rgb, pcl::Indices (),no_of_neighbors,indices,dists);
pcl::Indices k_indices;
k_indices.resize (no_of_neighbors);
std::vector<float> k_distances;
k_distances.resize (no_of_neighbors);
pcl::Indices k_indices_t;
k_indices_t.resize (no_of_neighbors);
std::vector<float> k_distances_t;
k_distances_t.resize (no_of_neighbors);
for (std::size_t i = 0; i < cloud_rgb.size (); ++i)
{
kdtree.nearestKSearchT<pcl::PointXYZRGB> (cloud_rgb[i], no_of_neighbors, k_indices_t, k_distances_t);
kdtree.nearestKSearch (cloud_big[i], no_of_neighbors, k_indices, k_distances);
EXPECT_EQ (k_indices.size (), indices[i].size ());
EXPECT_EQ (k_distances.size (), dists[i].size ());
for (std::size_t j=0; j< no_of_neighbors; j++)
{
EXPECT_TRUE (k_indices[j] == indices[i][j] || k_distances[j] == dists[i][j]);
EXPECT_EQ (k_indices[j], k_indices_t[j]);
EXPECT_EQ (k_distances[j], k_distances_t[j]);
}
}
}
/* Test for KdTree nearestKSearch with multiple query points */
TEST (PCL, KdTree_multipointKnnSearch)
{
unsigned int no_of_neighbors = 20;
pcl::search::KdTree<PointXYZ> kdtree;
//kdtree.initSearchDS ();
kdtree.setInputCloud (cloud_big.makeShared ());
std::vector< std::vector< float > > dists;
std::vector< pcl::Indices > indices;
kdtree.nearestKSearch (cloud_big, pcl::Indices (),no_of_neighbors,indices,dists);
pcl::Indices k_indices;
k_indices.resize (no_of_neighbors);
std::vector<float> k_distances;
k_distances.resize (no_of_neighbors);
for (std::size_t i = 0; i < cloud_big.size (); ++i)
{
kdtree.nearestKSearch (cloud_big[i], no_of_neighbors, k_indices, k_distances);
EXPECT_EQ (k_indices.size (), indices[i].size ());
EXPECT_EQ (k_distances.size (), dists[i].size ());
for (std::size_t j=0; j< no_of_neighbors; j++)
{
EXPECT_TRUE( k_indices[j]==indices[i][j] || k_distances[j] == dists[i][j]);
}
}
}
int
main (int argc, char** argv)
{
testing::InitGoogleTest (&argc, argv);
init ();
// Testing using explicit instantiation of inherited class
pcl::search::KdTree<PointXYZ> kdtree;
kdtree.setInputCloud (cloud.makeShared ());
return (RUN_ALL_TESTS ());
}
|
