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Svm.java
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Svm.java
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package org.genericsystem.cv;
import java.io.File;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Size;
import org.opencv.core.TermCriteria;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.ml.Ml;
import org.opencv.ml.SVM;
public class Svm {
static {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
}
private final static String imgClassDirectory = "tmp";
public static void main(String[] args) {
Mat classes = new Mat();
Mat trainingData = new Mat();
Mat trainingImages = new Mat();
Mat trainingLabels = new Mat();
SVM clasificador;
Size size = new Size(16, 16);
System.out.println(new File(imgClassDirectory + "/positives/").listFiles().length);
System.out.println(new File(imgClassDirectory + "/negatives/").listFiles().length);
for (File file : new File(imgClassDirectory + "/positives/").listFiles()) {
if (file.getName().endsWith(".png")) {
Mat img = Imgcodecs.imread(file.getPath());
Imgproc.cvtColor(img, img, Imgproc.COLOR_BGR2GRAY);
Imgproc.resize(img, img, size);
img = img.reshape(1, 1);
trainingImages.push_back(img);
trainingLabels.push_back(Mat.ones(new Size(1, 1), CvType.CV_32S));
}
}
for (File file : new File(imgClassDirectory + "/negatives/").listFiles()) {
if (file.getName().endsWith(".png")) {
Mat img = Imgcodecs.imread(file.getPath());
Imgproc.cvtColor(img, img, Imgproc.COLOR_BGR2GRAY);
Imgproc.resize(img, img, size);
img = img.reshape(1, 1);
trainingImages.push_back(img);
trainingLabels.push_back(Mat.zeros(new Size(1, 1), CvType.CV_32S));
}
}
trainingImages.convertTo(trainingData, CvType.CV_32FC1);
trainingLabels.copyTo(classes);
clasificador = SVM.create();
clasificador.setType(SVM.C_SVC);
clasificador.setTermCriteria(new TermCriteria(TermCriteria.MAX_ITER, 100, 1e-6));
clasificador.setKernel(SVM.LINEAR);
System.out.println(trainingData.rows());
System.out.println(classes.rows());
clasificador.train(trainingData, Ml.ROW_SAMPLE, trainingLabels);
for (File file : new File(imgClassDirectory + "/samples/").listFiles()) {
if (file.getName().endsWith(".png")) {
Mat img = Imgcodecs.imread(file.getPath());
Imgproc.cvtColor(img, img, Imgproc.COLOR_BGR2GRAY);
Imgproc.resize(img, img, size);
img = img.reshape(1, 1);
img.convertTo(img, CvType.CV_32FC1);
System.out.println(file.getName() + " : " + clasificador.predict(img));
}
}
}
// static int test2()
// {
// // Data for visual representation
// int width = 512, height = 512;
// Mat image = Mat::zeros(height, width, CV_8UC3);
//
// // Set up training data
// int labels[] = {1, -1, -1, -1};
// Mat labelsMat(4, 1, CV_32SC1, labels);
//
// float trainingData[][] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
// Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
//
// // Set up SVM's parameters
// Ptr svm = ml::SVM::create();
// svm->setType(ml::SVM::C_SVC);
// svm->setKernel(ml::SVM::LINEAR);
// svm->setTermCriteria(cv::TermCriteria(CV_TERMCRIT_ITER, 100, 1e-6));
//
// // Train the SVM
// svm->train(trainingDataMat, ml::ROW_SAMPLE, labelsMat);
//
// Vec3b green(0,255,0), blue (255,0,0);
// // Show the decision regions given by the SVM
// for (int i = 0; i < image.rows; ++i)
// for (int j = 0; j < image.cols; ++j)
// {
// Mat sampleMat = (Mat_(1,2) << j,i); float response = svm->predict(sampleMat);
//
// if (response == 1)
// image.at(i,j) = green;
// else if (response == -1)
// image.at(i,j) = blue;
// }
//
// // Show the training data
// int thickness = -1;
// int lineType = 8;
// circle( image, Point(501, 10), 5, Scalar( 0, 0, 0), thickness, lineType);
// circle( image, Point(255, 10), 5, Scalar(255, 255, 255), thickness, lineType);
// circle( image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType);
// circle( image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness, lineType);
//
// // Show support vectors
// thickness = 2;
// lineType = 8;
//
// Mat supVecs = svm->getSupportVectors();
// int c = supVecs.rows;
//
// for (int i = 0; i < c; ++i)
// {
// std::vector v;
// v.assign(supVecs.row(i).datastart, supVecs.row(i).dataend);
// circle( image, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thickness, lineType);
// }
//
// imwrite("result.png", image); // save the image
//
// imshow("SVM Simple Example", image); // show it to the user
// waitKey(0);
//
// }
}