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
|
/*
* ViSP, open source Visual Servoing Platform software.
* Copyright (C) 2005 - 2024 by Inria. All rights reserved.
*
* This software is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
* See the file LICENSE.txt at the root directory of this source
* distribution for additional information about the GNU GPL.
*
* For using ViSP with software that can not be combined with the GNU
* GPL, please contact Inria about acquiring a ViSP Professional
* Edition License.
*
* See https://visp.inria.fr for more information.
*
* This software was developed at:
* Inria Rennes - Bretagne Atlantique
* Campus Universitaire de Beaulieu
* 35042 Rennes Cedex
* France
*
* If you have questions regarding the use of this file, please contact
* Inria at visp@inria.fr
*
* This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE
* WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
*/
#include "vpTutoMeanSquareFitting.h"
#if (VISP_CXX_STANDARD >= VISP_CXX_STANDARD_11)
#ifndef DOXYGEN_SHOULD_SKIP_THIS
namespace tutorial
{
#ifdef ENABLE_VISP_NAMESPACE
using namespace VISP_NAMESPACE_NAME;
#endif
vpTutoMeanSquareFitting::vpTutoMeanSquareFitting(const unsigned int °ree, const unsigned int &height, const unsigned int &width)
: m_degree(degree)
, m_height(static_cast<unsigned int>(height))
, m_width(static_cast<unsigned int>(width))
, m_model(degree, height, width)
, m_isFitted(false)
{ }
//! [Solve_LMS_system]
void vpTutoMeanSquareFitting::fit(const std::vector<vpImagePoint> &pts)
{
vpMatrix A; // The matrix that contains the u^i
vpMatrix X; // The matrix we want to estimate, that contains the polynomial coefficients.
vpMatrix b; // The matrix that contains the v values
// Fill the matrices that form the system we want to solve
vpTutoParabolaModel::fillSystem(m_degree, m_height, m_width, pts, A, b);
// Compute the parabola coefficients using the least-mean-square method.
X = A.pseudoInverse() * b;
m_model = vpTutoParabolaModel(X, m_height, m_width);
m_isFitted = true;
}
//! [Solve_LMS_system]
double vpTutoMeanSquareFitting::evaluate(const std::vector<vpImagePoint> &pts)
{
if (!m_isFitted) {
throw(vpException(vpException::notInitialized, "fit() has not been called."));
}
unsigned int nbPts = static_cast<unsigned int>(pts.size());
// Compute the mean absolute error
double meanSquareError = 0.f;
for (unsigned int i = 0; i < nbPts; ++i) {
double squareError = evaluate(pts[i]);
meanSquareError += squareError;
}
meanSquareError /= static_cast<double>(nbPts);
return std::sqrt(meanSquareError);
}
double vpTutoMeanSquareFitting::evaluate(const vpImagePoint &pt)
{
if (!m_isFitted) {
throw(vpException(vpException::notInitialized, "fit() has not been called."));
}
double u = pt.get_u();
double v = pt.get_v();
double v_model = model(u);
double error = v - v_model;
double squareError = error * error;
return squareError;
}
double vpTutoMeanSquareFitting::model(const double &u)
{
if (!m_isFitted) {
throw(vpException(vpException::notInitialized, "fit() has not been called."));
}
double v = m_model.eval(u);
return v;
}
}
#endif
#else
void dummy_vpTutoMeanSquareFitting() { }
#endif
|
