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Joachim
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samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java Normal file
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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 IntroductionToSVMDemo {
public static void main(String[] args) {
// Load the native OpenCV library
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
// Set up training data
//! [setup1]
int[] labels = { 1, -1, -1, -1 };
float[] trainingData = { 501, 10, 255, 10, 501, 255, 10, 501 };
//! [setup1]
//! [setup2]
Mat trainingDataMat = new Mat(4, 2, CvType.CV_32FC1);
trainingDataMat.put(0, 0, trainingData);
Mat labelsMat = new Mat(4, 1, CvType.CV_32SC1);
labelsMat.put(0, 0, labels);
//! [setup2]
// Train the SVM
//! [init]
SVM svm = SVM.create();
svm.setType(SVM.C_SVC);
svm.setKernel(SVM.LINEAR);
svm.setTermCriteria(new TermCriteria(TermCriteria.MAX_ITER, 100, 1e-6));
//! [init]
//! [train]
svm.train(trainingDataMat, Ml.ROW_SAMPLE, labelsMat);
//! [train]
// Data for visual representation
int width = 512, height = 512;
Mat image = Mat.zeros(height, width, CvType.CV_8UC3);
// Show the decision regions given by the SVM
//! [show]
byte[] imageData = new byte[(int) (image.total() * image.channels())];
Mat sampleMat = new Mat(1, 2, CvType.CV_32F);
float[] sampleMatData = new float[(int) (sampleMat.total() * sampleMat.channels())];
for (int i = 0; i < image.rows(); i++) {
for (int j = 0; j < image.cols(); j++) {
sampleMatData[0] = j;
sampleMatData[1] = i;
sampleMat.put(0, 0, sampleMatData);
float response = svm.predict(sampleMat);
if (response == 1) {
imageData[(i * image.cols() + j) * image.channels()] = 0;
imageData[(i * image.cols() + j) * image.channels() + 1] = (byte) 255;
imageData[(i * image.cols() + j) * image.channels() + 2] = 0;
} else if (response == -1) {
imageData[(i * image.cols() + j) * image.channels()] = (byte) 255;
imageData[(i * image.cols() + j) * image.channels() + 1] = 0;
imageData[(i * image.cols() + j) * image.channels() + 2] = 0;
}
}
}
image.put(0, 0, imageData);
//! [show]
// Show the training data
//! [show_data]
int thickness = -1;
int lineType = Imgproc.LINE_8;
Imgproc.circle(image, new Point(501, 10), 5, new Scalar(0, 0, 0), thickness, lineType, 0);
Imgproc.circle(image, new Point(255, 10), 5, new Scalar(255, 255, 255), thickness, lineType, 0);
Imgproc.circle(image, new Point(501, 255), 5, new Scalar(255, 255, 255), thickness, lineType, 0);
Imgproc.circle(image, new Point(10, 501), 5, new Scalar(255, 255, 255), thickness, lineType, 0);
//! [show_data]
// Show support vectors
//! [show_vectors]
thickness = 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(image, new Point(svData[i * sv.cols()], svData[i * sv.cols() + 1]), 6,
new Scalar(128, 128, 128), thickness, lineType, 0);
}
//! [show_vectors]
Imgcodecs.imwrite("result.png", image); // save the image
HighGui.imshow("SVM Simple Example", image); // show it to the user
HighGui.waitKey();
System.exit(0);
}
}