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
|
import java.util.Random;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.core.TermCriteria;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.ml.Ml;
import org.opencv.ml.SVM;
public class NonLinearSVMsDemo {
public static final int NTRAINING_SAMPLES = 100;
public static final float FRAC_LINEAR_SEP = 0.9f;
public static void main(String[] args) {
// Load the native OpenCV library
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
System.out.println("\n--------------------------------------------------------------------------");
System.out.println("This program shows Support Vector Machines for Non-Linearly Separable Data. ");
System.out.println("--------------------------------------------------------------------------\n");
// Data for visual representation
int width = 512, height = 512;
Mat I = Mat.zeros(height, width, CvType.CV_8UC3);
// --------------------- 1. Set up training data randomly---------------------------------------
Mat trainData = new Mat(2 * NTRAINING_SAMPLES, 2, CvType.CV_32F);
Mat labels = new Mat(2 * NTRAINING_SAMPLES, 1, CvType.CV_32S);
Random rng = new Random(100); // Random value generation class
// Set up the linearly separable part of the training data
int nLinearSamples = (int) (FRAC_LINEAR_SEP * NTRAINING_SAMPLES);
//! [setup1]
// Generate random points for the class 1
Mat trainClass = trainData.rowRange(0, nLinearSamples);
// The x coordinate of the points is in [0, 0.4)
Mat c = trainClass.colRange(0, 1);
float[] cData = new float[(int) (c.total() * c.channels())];
double[] cDataDbl = rng.doubles(cData.length, 0, 0.4f * width).toArray();
for (int i = 0; i < cData.length; i++) {
cData[i] = (float) cDataDbl[i];
}
c.put(0, 0, cData);
// The y coordinate of the points is in [0, 1)
c = trainClass.colRange(1, 2);
cData = new float[(int) (c.total() * c.channels())];
cDataDbl = rng.doubles(cData.length, 0, height).toArray();
for (int i = 0; i < cData.length; i++) {
cData[i] = (float) cDataDbl[i];
}
c.put(0, 0, cData);
// Generate random points for the class 2
trainClass = trainData.rowRange(2 * NTRAINING_SAMPLES - nLinearSamples, 2 * NTRAINING_SAMPLES);
// The x coordinate of the points is in [0.6, 1]
c = trainClass.colRange(0, 1);
cData = new float[(int) (c.total() * c.channels())];
cDataDbl = rng.doubles(cData.length, 0.6 * width, width).toArray();
for (int i = 0; i < cData.length; i++) {
cData[i] = (float) cDataDbl[i];
}
c.put(0, 0, cData);
// The y coordinate of the points is in [0, 1)
c = trainClass.colRange(1, 2);
cData = new float[(int) (c.total() * c.channels())];
cDataDbl = rng.doubles(cData.length, 0, height).toArray();
for (int i = 0; i < cData.length; i++) {
cData[i] = (float) cDataDbl[i];
}
c.put(0, 0, cData);
//! [setup1]
// ------------------ Set up the non-linearly separable part of the training data ---------------
//! [setup2]
// Generate random points for the classes 1 and 2
trainClass = trainData.rowRange(nLinearSamples, 2 * NTRAINING_SAMPLES - nLinearSamples);
// The x coordinate of the points is in [0.4, 0.6)
c = trainClass.colRange(0, 1);
cData = new float[(int) (c.total() * c.channels())];
cDataDbl = rng.doubles(cData.length, 0.4 * width, 0.6 * width).toArray();
for (int i = 0; i < cData.length; i++) {
cData[i] = (float) cDataDbl[i];
}
c.put(0, 0, cData);
// The y coordinate of the points is in [0, 1)
c = trainClass.colRange(1, 2);
cData = new float[(int) (c.total() * c.channels())];
cDataDbl = rng.doubles(cData.length, 0, height).toArray();
for (int i = 0; i < cData.length; i++) {
cData[i] = (float) cDataDbl[i];
}
c.put(0, 0, cData);
//! [setup2]
// ------------------------- Set up the labels for the classes---------------------------------
labels.rowRange(0, NTRAINING_SAMPLES).setTo(new Scalar(1)); // Class 1
labels.rowRange(NTRAINING_SAMPLES, 2 * NTRAINING_SAMPLES).setTo(new Scalar(2)); // Class 2
// ------------------------ 2. Set up the support vector machines parameters--------------------
System.out.println("Starting training process");
//! [init]
SVM svm = SVM.create();
svm.setType(SVM.C_SVC);
svm.setC(0.1);
svm.setKernel(SVM.LINEAR);
svm.setTermCriteria(new TermCriteria(TermCriteria.MAX_ITER, (int) 1e7, 1e-6));
//! [init]
// ------------------------ 3. Train the svm----------------------------------------------------
//! [train]
svm.train(trainData, Ml.ROW_SAMPLE, labels);
//! [train]
System.out.println("Finished training process");
// ------------------------ 4. Show the decision regions----------------------------------------
//! [show]
byte[] IData = new byte[(int) (I.total() * I.channels())];
Mat sampleMat = new Mat(1, 2, CvType.CV_32F);
float[] sampleMatData = new float[(int) (sampleMat.total() * sampleMat.channels())];
for (int i = 0; i < I.rows(); i++) {
for (int j = 0; j < I.cols(); j++) {
sampleMatData[0] = j;
sampleMatData[1] = i;
sampleMat.put(0, 0, sampleMatData);
float response = svm.predict(sampleMat);
if (response == 1) {
IData[(i * I.cols() + j) * I.channels()] = 0;
IData[(i * I.cols() + j) * I.channels() + 1] = 100;
IData[(i * I.cols() + j) * I.channels() + 2] = 0;
} else if (response == 2) {
IData[(i * I.cols() + j) * I.channels()] = 100;
IData[(i * I.cols() + j) * I.channels() + 1] = 0;
IData[(i * I.cols() + j) * I.channels() + 2] = 0;
}
}
}
I.put(0, 0, IData);
//! [show]
// ----------------------- 5. Show the training data--------------------------------------------
//! [show_data]
int thick = -1;
int lineType = Imgproc.LINE_8;
float px, py;
// Class 1
float[] trainDataData = new float[(int) (trainData.total() * trainData.channels())];
trainData.get(0, 0, trainDataData);
for (int i = 0; i < NTRAINING_SAMPLES; i++) {
px = trainDataData[i * trainData.cols()];
py = trainDataData[i * trainData.cols() + 1];
Imgproc.circle(I, new Point(px, py), 3, new Scalar(0, 255, 0), thick, lineType, 0);
}
// Class 2
for (int i = NTRAINING_SAMPLES; i < 2 * NTRAINING_SAMPLES; ++i) {
px = trainDataData[i * trainData.cols()];
py = trainDataData[i * trainData.cols() + 1];
Imgproc.circle(I, new Point(px, py), 3, new Scalar(255, 0, 0), thick, lineType, 0);
}
//! [show_data]
// ------------------------- 6. Show support vectors--------------------------------------------
//! [show_vectors]
thick = 2;
Mat sv = svm.getUncompressedSupportVectors();
float[] svData = new float[(int) (sv.total() * sv.channels())];
sv.get(0, 0, svData);
for (int i = 0; i < sv.rows(); i++) {
Imgproc.circle(I, new Point(svData[i * sv.cols()], svData[i * sv.cols() + 1]), 6, new Scalar(128, 128, 128),
thick, lineType, 0);
}
//! [show_vectors]
Imgcodecs.imwrite("result.png", I); // save the Image
HighGui.imshow("SVM for Non-Linear Training Data", I); // show it to the user
HighGui.waitKey();
System.exit(0);
}
}
|
