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