Visual Servoing Platform version 3.5.0
testTukeyEstimator.cpp
1/****************************************************************************
2 *
3 * ViSP, open source Visual Servoing Platform software.
4 * Copyright (C) 2005 - 2019 by Inria. All rights reserved.
5 *
6 * This software is free software; you can redistribute it and/or modify
7 * it under the terms of the GNU General Public License as published by
8 * the Free Software Foundation; either version 2 of the License, or
9 * (at your option) any later version.
10 * See the file LICENSE.txt at the root directory of this source
11 * distribution for additional information about the GNU GPL.
12 *
13 * For using ViSP with software that can not be combined with the GNU
14 * GPL, please contact Inria about acquiring a ViSP Professional
15 * Edition License.
16 *
17 * See http://visp.inria.fr for more information.
18 *
19 * This software was developed at:
20 * Inria Rennes - Bretagne Atlantique
21 * Campus Universitaire de Beaulieu
22 * 35042 Rennes Cedex
23 * France
24 *
25 * If you have questions regarding the use of this file, please contact
26 * Inria at visp@inria.fr
27 *
28 * This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE
29 * WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
30 *
31 * Description:
32 * Test Tukey M-Estimator.
33 *
34 *****************************************************************************/
35
42#include <cstdlib>
43#include <iostream>
44#include <time.h>
45#include <visp3/core/vpConfig.h>
46#include <visp3/core/vpGaussRand.h>
47#include <visp3/core/vpRobust.h>
48#include <visp3/mbt/vpMbtTukeyEstimator.h>
49
50int main()
51{
52 size_t nb_elements = 1000;
53 int nb_iterations = 100;
54 double stdev = 0.5, mean = 0.0, noise_threshold = 1e-3;
55
56 vpGaussRand noise(stdev, mean);
57 noise.seed((unsigned int)time(NULL));
58
59 vpColVector residues_col((unsigned int)nb_elements);
60 vpColVector weights_col, weights_col_save;
61 for (size_t i = 0; i < nb_elements; i++) {
62 residues_col[(unsigned int)i] = noise();
63 }
64
65 vpRobust robust;
66 robust.setMinMedianAbsoluteDeviation(noise_threshold);
67 double t_robust = vpTime::measureTimeMs();
68 for (int i = 0; i < nb_iterations; i++) {
69 robust.MEstimator(vpRobust::TUKEY, residues_col, weights_col);
70 }
71 t_robust = vpTime::measureTimeMs() - t_robust;
72 {
73 vpMbtTukeyEstimator<double> tukey_estimator;
74 std::vector<double> residues(nb_elements);
75 for (size_t i = 0; i < residues.size(); i++) {
76 residues[i] = residues_col[(unsigned int)i];
77 }
78
79 std::vector<double> weights;
80 double t = vpTime::measureTimeMs();
81 for (int i = 0; i < nb_iterations; i++) {
82 tukey_estimator.MEstimator(residues, weights, noise_threshold);
83 }
84 t = vpTime::measureTimeMs() - t;
85
86 std::cout << "t_robust=" << t_robust << " ms ; t (double)=" << t << " ; ratio=" << (t_robust / t) << std::endl;
87
88 for (size_t i = 0; i < weights.size(); i++) {
89 if (!vpMath::equal(weights[i], weights_col[(unsigned int)i], noise_threshold)) {
90 std::cerr << "Difference between vpRobust::TUKEY and "
91 "vpMbtTukeyEstimator (double)!"
92 << std::endl;
93 std::cerr << "weights_col[" << i << "]=" << weights_col[(unsigned int)i] << std::endl;
94 std::cerr << "weights[" << i << "]=" << weights[i] << std::endl;
95 return EXIT_FAILURE;
96 }
97 }
98 }
99
100 // Generate again for weights != 1
101 for (size_t i = 0; i < nb_elements; i++) {
102 residues_col[(unsigned int)i] = noise();
103 }
104 weights_col_save = weights_col;
105 t_robust = vpTime::measureTimeMs();
106 for (int i = 0; i < nb_iterations; i++) {
107 robust.MEstimator(vpRobust::TUKEY, residues_col, weights_col);
108 }
109 t_robust = vpTime::measureTimeMs() - t_robust;
110
111 {
112 vpMbtTukeyEstimator<float> tukey_estimator;
113 std::vector<float> residues(nb_elements);
114 std::vector<float> weights(nb_elements);
115 for (size_t i = 0; i < residues.size(); i++) {
116 residues[i] = (float)residues_col[(unsigned int)i];
117 weights[i] = (float)weights_col_save[(unsigned int)i];
118 }
119
120 double t = vpTime::measureTimeMs();
121 for (int i = 0; i < nb_iterations; i++) {
122 tukey_estimator.MEstimator(residues, weights, (float)noise_threshold);
123 }
124 t = vpTime::measureTimeMs() - t;
125
126 std::cout << "t_robust=" << t_robust << " ms ; t (float)=" << t << " ; ratio=" << (t_robust / t) << std::endl;
127
128 for (size_t i = 0; i < weights.size(); i++) {
129 if (!vpMath::equal(weights[i], weights_col[(unsigned int)i], noise_threshold)) {
130 std::cerr << "Difference between vpRobust::TUKEY and "
131 "vpMbtTukeyEstimator (float)!"
132 << std::endl;
133 std::cerr << "weights_col[" << i << "]=" << weights_col[(unsigned int)i] << std::endl;
134 std::cerr << "weights[" << i << "]=" << weights[i] << std::endl;
135 return EXIT_FAILURE;
136 }
137 }
138 }
139
140 // Generate again for weights != 1 and vpColVector type
141 for (size_t i = 0; i < nb_elements; i++) {
142 residues_col[(unsigned int)i] = noise();
143 }
144 weights_col_save = weights_col;
145 t_robust = vpTime::measureTimeMs();
146 for (int i = 0; i < nb_iterations; i++) {
147 robust.MEstimator(vpRobust::TUKEY, residues_col, weights_col);
148 }
149 t_robust = vpTime::measureTimeMs() - t_robust;
150
151 {
152 vpMbtTukeyEstimator<double> tukey_estimator;
153 vpColVector residues = residues_col;
154 vpColVector weights = weights_col_save;
155
156 double t = vpTime::measureTimeMs();
157 for (int i = 0; i < nb_iterations; i++) {
158 tukey_estimator.MEstimator(residues, weights, noise_threshold);
159 }
160 t = vpTime::measureTimeMs() - t;
161
162 std::cout << "t_robust=" << t_robust << " ms ; t (vpColVector)=" << t << " ; ratio=" << (t_robust / t) << std::endl;
163
164 for (size_t i = 0; i < weights.size(); i++) {
165 if (!vpMath::equal(weights[(unsigned int)i], weights_col[(unsigned int)i], noise_threshold)) {
166 std::cerr << "Difference between vpRobust::TUKEY and "
167 "vpMbtTukeyEstimator (float)!"
168 << std::endl;
169 std::cerr << "weights_col[" << i << "]=" << weights_col[(unsigned int)i] << std::endl;
170 std::cerr << "weights[" << i << "]=" << weights[(unsigned int)i] << std::endl;
171 return EXIT_FAILURE;
172 }
173 }
174 }
175
176 std::cout << "vpMbtTukeyEstimator returns the same values than vpRobust::TUKEY." << std::endl;
177 return EXIT_SUCCESS;
178}
unsigned int size() const
Return the number of elements of the 2D array.
Definition: vpArray2D.h:291
Implementation of column vector and the associated operations.
Definition: vpColVector.h:131
Class for generating random number with normal probability density.
Definition: vpGaussRand.h:121
static bool equal(double x, double y, double s=0.001)
Definition: vpMath.h:295
Contains an M-estimator and various influence function.
Definition: vpRobust.h:89
@ TUKEY
Tukey influence function.
Definition: vpRobust.h:93
void MEstimator(const vpRobustEstimatorType method, const vpColVector &residues, vpColVector &weights)
Definition: vpRobust.cpp:137
void setMinMedianAbsoluteDeviation(double mad_min)
Definition: vpRobust.h:161
VISP_EXPORT double measureTimeMs()