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
|
/*
* Copyright (C) 2005-2020 Centre National d'Etudes Spatiales (CNES)
*
* This file is part of Orfeo Toolbox
*
* https://www.orfeo-toolbox.org/
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef otbCvRTreesWrapper_h
#define otbCvRTreesWrapper_h
#include "otbOpenCVUtils.h"
#include <vector>
namespace otb
{
/** \class CvRTreesWrapper
* \brief Wrapper for OpenCV Random Trees
*
* \ingroup OTBSupervised
*/
class OTBSupervised_EXPORT CvRTreesWrapper
: public cv::ml::RTrees
{
public:
typedef std::vector<unsigned int> VotesVectorType;
CvRTreesWrapper();
~CvRTreesWrapper() override = default;
/** Compute the number of votes for each class. */
void get_votes(const cv::Mat& sample, const cv::Mat& missing, VotesVectorType& vote_count) const;
/** Predict the confidence of the classifcation by computing the proportion
of trees which voted for the majority class.
*/
float predict_confidence(const cv::Mat& sample, const cv::Mat& missing = cv::Mat()) const;
/** Predict the confidence margin of the classifcation by computing the
difference in votes between the first and second most voted classes.
This measure is preferred to the proportion of votes of the majority
class, since it provides information about the conflict between the
most likely classes.
*/
float predict_margin(const cv::Mat& sample, const cv::Mat& missing = cv::Mat()) const;
#define OTB_CV_WRAP_PROPERTY(type, name) \
virtual type get##name() const override; \
virtual void set##name(type val) override;
#define OTB_CV_WRAP_PROPERTY_REF(type, name) \
virtual type get##name() const override; \
virtual void set##name(const type& val) override;
#define OTB_CV_WRAP_CSTREF_GET(type, name) virtual const type& get##name() const override;
// TODO : wrap all method used
virtual int getVarCount() const override;
virtual bool isTrained() const override;
virtual bool isClassifier() const override;
OTB_CV_WRAP_PROPERTY(int, MaxCategories)
OTB_CV_WRAP_PROPERTY(int, MaxDepth)
OTB_CV_WRAP_PROPERTY(int, MinSampleCount)
OTB_CV_WRAP_PROPERTY(bool, UseSurrogates)
// warning: CV fold crash in openCV 3
OTB_CV_WRAP_PROPERTY(int, CVFolds)
OTB_CV_WRAP_PROPERTY(bool, Use1SERule)
OTB_CV_WRAP_PROPERTY(bool, TruncatePrunedTree)
OTB_CV_WRAP_PROPERTY(float, RegressionAccuracy)
OTB_CV_WRAP_PROPERTY(bool, CalculateVarImportance)
OTB_CV_WRAP_PROPERTY(int, ActiveVarCount)
OTB_CV_WRAP_PROPERTY_REF(cv::Mat, Priors)
OTB_CV_WRAP_PROPERTY_REF(cv::TermCriteria, TermCriteria)
OTB_CV_WRAP_CSTREF_GET(std::vector<int>, Roots)
OTB_CV_WRAP_CSTREF_GET(std::vector<cv::ml::DTrees::Node>, Nodes)
OTB_CV_WRAP_CSTREF_GET(std::vector<cv::ml::DTrees::Split>, Splits)
OTB_CV_WRAP_CSTREF_GET(std::vector<int>, Subsets)
#ifdef OTB_OPENCV_4
virtual void getVotes(cv::InputArray samples, cv::OutputArray results, int flags) const override;
#endif
virtual cv::Mat getVarImportance() const override;
virtual cv::String getDefaultName() const override;
virtual void read(const cv::FileNode& fn) override;
virtual void write(cv::FileStorage& fs) const override;
virtual void save(const cv::String& filename) const override;
virtual bool train(cv::InputArray samples, int layout, cv::InputArray responses) override;
virtual bool train(const cv::Ptr<cv::ml::TrainData>& trainData, int flags = 0) override;
virtual float predict(cv::InputArray samples, cv::OutputArray results = cv::noArray(), int flags = 0) const override;
static cv::Ptr<CvRTreesWrapper> create();
#undef OTB_CV_WRAP_PROPERTY
#undef OTB_CV_WRAP_PROPERTY_REF
#undef OTB_CV_WRAP_CSTREF_GET
private:
cv::Ptr<cv::ml::RTrees> m_Impl;
};
}
#endif
|
