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
|
/*
* 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.
*/
#include "otbVectorImage.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "otbImageClassificationFilter.h"
#include "otbSharkKMeansMachineLearningModelFactory.h"
#include <random>
#include <chrono>
const unsigned int Dimension = 2;
typedef float PixelType;
typedef unsigned short LabeledPixelType;
typedef otb::VectorImage<PixelType, Dimension> ImageType;
typedef otb::Image<LabeledPixelType, Dimension> LabeledImageType;
typedef otb::ImageClassificationFilter<ImageType, LabeledImageType> ClassificationFilterType;
typedef ClassificationFilterType::ModelType ModelType;
typedef ClassificationFilterType::ValueType ValueType;
typedef ClassificationFilterType::LabelType LabelType;
typedef otb::SharkKMeansMachineLearningModelFactory<ValueType, LabelType> MachineLearningModelFactoryType;
typedef otb::ImageFileReader<ImageType> ReaderType;
typedef otb::ImageFileReader<LabeledImageType> MaskReaderType;
typedef otb::ImageFileWriter<LabeledImageType> WriterType;
typedef otb::SharkKMeansMachineLearningModel<PixelType, short unsigned int> MachineLearningModelType;
typedef MachineLearningModelType::InputValueType LocalInputValueType;
typedef MachineLearningModelType::InputSampleType LocalInputSampleType;
typedef MachineLearningModelType::InputListSampleType LocalInputListSampleType;
typedef MachineLearningModelType::TargetValueType LocalTargetValueType;
typedef MachineLearningModelType::TargetSampleType LocalTargetSampleType;
typedef MachineLearningModelType::TargetListSampleType LocalTargetListSampleType;
void generateSamples(unsigned int num_classes, unsigned int num_samples, unsigned int num_features, LocalInputListSampleType* samples,
LocalTargetListSampleType* labels)
{
std::default_random_engine randomEngine;
std::uniform_int_distribution<int> label_distribution(1, num_classes);
std::uniform_int_distribution<int> feat_distribution(0, 256);
for (size_t scount = 0; scount < num_samples; ++scount)
{
LabeledPixelType label = label_distribution(randomEngine);
LocalInputSampleType sample(num_features);
for (unsigned int i = 0; i < num_features; ++i)
sample[i] = feat_distribution(randomEngine);
samples->SetMeasurementVectorSize(num_features);
samples->PushBack(sample);
labels->PushBack(label);
}
}
void buildModel(unsigned int num_classes, unsigned int num_samples, unsigned int num_features, std::string modelfname)
{
LocalInputListSampleType::Pointer samples = LocalInputListSampleType::New();
LocalTargetListSampleType::Pointer labels = LocalTargetListSampleType::New();
std::cout << "Sample generation\n";
generateSamples(num_classes, num_samples, num_features, samples, labels);
MachineLearningModelType::Pointer classifier = MachineLearningModelType::New();
classifier->SetInputListSample(samples);
classifier->SetTargetListSample(labels);
classifier->SetRegressionMode(false);
classifier->SetK(3);
std::cout << "Training\n";
using TimeT = std::chrono::milliseconds;
auto start = std::chrono::system_clock::now();
classifier->Train();
auto duration = std::chrono::duration_cast<TimeT>(std::chrono::system_clock::now() - start);
auto elapsed = duration.count();
std::cout << "Training took " << elapsed << " ms\n";
classifier->Save(modelfname);
}
int otbSharkUnsupervisedImageClassificationFilter(int argc, char* argv[])
{
if (argc < 5 || argc > 7)
{
std::cout << "Usage: input_image output_image output_confidence batchmode [in_model_name] [mask_name]\n";
}
std::string imfname = argv[1];
std::string outfname = argv[2];
std::string conffname = argv[3];
bool batch = (std::string(argv[4]) == "1");
std::string modelfname = "/tmp/rf_model.txt";
std::string maskfname{};
MaskReaderType::Pointer mask_reader = MaskReaderType::New();
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(imfname);
reader->UpdateOutputInformation();
auto num_features = reader->GetOutput()->GetNumberOfComponentsPerPixel();
std::cout << "Image has " << num_features << " bands\n";
if (argc > 5)
{
modelfname = argv[5];
}
else
{
buildModel(3, 1000, num_features, modelfname);
}
ClassificationFilterType::Pointer filter = ClassificationFilterType::New();
MachineLearningModelType::Pointer model = MachineLearningModelType::New();
if (!model->CanReadFile(modelfname))
{
std::cerr << "Unable to read the model : " << modelfname << "\n";
return EXIT_FAILURE;
}
filter->SetModel(model);
filter->SetInput(reader->GetOutput());
if (argc == 7)
{
maskfname = argv[6];
mask_reader->SetFileName(maskfname);
filter->SetInputMask(mask_reader->GetOutput());
}
WriterType::Pointer writer = WriterType::New();
writer->SetInput(filter->GetOutput());
writer->SetFileName(outfname);
std::cout << "Classification\n";
filter->SetBatchMode(batch);
filter->SetUseConfidenceMap(true);
using TimeT = std::chrono::milliseconds;
auto start = std::chrono::system_clock::now();
writer->Update();
auto duration = std::chrono::duration_cast<TimeT>(std::chrono::system_clock::now() - start);
auto elapsed = duration.count();
std::cout << "Classification took " << elapsed << " ms\n";
auto confWriter = otb::ImageFileWriter<ClassificationFilterType::ConfidenceImageType>::New();
confWriter->SetInput(filter->GetOutputConfidence());
confWriter->SetFileName(conffname);
confWriter->Update();
return EXIT_SUCCESS;
}
|
