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LinesDetector3.java
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LinesDetector3.java
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package org.genericsystem.cv;
import java.util.Arrays;
import java.util.Collection;
import java.util.Iterator;
import java.util.List;
import java.util.concurrent.Executors;
import java.util.concurrent.ScheduledExecutorService;
import java.util.concurrent.TimeUnit;
import java.util.function.Function;
import javafx.scene.image.ImageView;
import javafx.scene.layout.GridPane;
import org.genericsystem.cv.lm.LevenbergImpl;
import org.genericsystem.cv.utils.Line;
import org.genericsystem.cv.utils.NativeLibraryLoader;
import org.genericsystem.cv.utils.Ransac;
import org.genericsystem.cv.utils.Ransac.Model;
import org.genericsystem.cv.utils.Tools;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfPoint2f;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.imgproc.Imgproc;
import org.opencv.utils.Converters;
import org.opencv.videoio.VideoCapture;
public class LinesDetector3 extends AbstractApp {
static {
NativeLibraryLoader.load();
}
public static void main(String[] args) {
launch(args);
}
private final VideoCapture capture = new VideoCapture(0);
private ScheduledExecutorService timer = Executors.newSingleThreadScheduledExecutor();
@Override
protected void fillGrid(GridPane mainGrid) {
Mat frame = new Mat();
capture.read(frame);
ImageView frameView = new ImageView(Tools.mat2jfxImage(frame));
mainGrid.add(frameView, 0, 0);
ImageView deskewedView = new ImageView(Tools.mat2jfxImage(frame));
mainGrid.add(deskewedView, 0, 1);
Mat dePerspectived = frame.clone();
timer.scheduleAtFixedRate(() -> {
try {
capture.read(frame);
Img grad = new Img(frame, false).morphologyEx(Imgproc.MORPH_GRADIENT, Imgproc.MORPH_ELLIPSE, new Size(2, 2)).otsu().morphologyEx(Imgproc.MORPH_CLOSE, Imgproc.MORPH_ELLIPSE, new Size(7, 7));
// Img grad = new Img(frame, false).canny(60, 180);
// Img grad = new Img(frame, false).bilateralFilter(20, 120, 120).bgr2Gray().adaptativeThresHold(255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C, Imgproc.THRESH_BINARY_INV, 11, 3)
// .morphologyEx(Imgproc.MORPH_CLOSE, Imgproc.MORPH_ELLIPSE, new Size(5, 5));
Lines lines = new Lines(grad.houghLinesP(1, Math.PI / 180, 10, 100, 10));
System.out.println("Average angle: " + lines.getMean() / Math.PI * 180);
if (lines.size() > 10) {
lines.draw(frame, new Scalar(0, 0, 255));
frameView.setImage(Tools.mat2jfxImage(frame));
Ransac<Line> ransac = lines.vanishingPointRansac(frame.width(), frame.height());
Mat vp_mat = (Mat) ransac.getBestModel().getParams()[0];
Point vp = new Point(vp_mat.get(0, 0)[0], vp_mat.get(1, 0)[0]);
// Matrix vpCalib = Matrix.convert(Lines.K.inv()).times(new Matrix(new double[][] { { vp.x }, { vp.y }, { 1d } }), 1);
Mat vpCalib = calibrate(Converters.vector_double_to_Mat(Arrays.asList(vp.x, vp.y, 1d))); // replace with vp_mat directly?
LevenbergImpl<Line> fitHost = new LevenbergImpl<>((datas, params) -> {
Mat lineMat = Lines.getLineMat(datas);
double di = params[0] * lineMat.get(0, 0)[0] + params[1] * lineMat.get(1, 0)[0] + params[2] * lineMat.get(2, 0)[0];
di /= (Math.sqrt(params[0] * params[0] + params[1] * params[1] + params[2] * params[2]) * Core.norm(lineMat, Core.NORM_L2));
return di * di;
}, ransac.getBestDataSet().values(), new double[] { vpCalib.get(0, 0)[0], vpCalib.get(1, 0)[0], vpCalib.get(2, 0)[0] });
double[] newVp = fitHost.getParams();
// Matrix result = Matrix.convert(Lines.K).times(new Matrix(new double[][] { { newVp[0] }, { newVp[1] }, { newVp[2] } }), 1);
Mat result = unCalibrate(Converters.vector_double_to_Mat(Arrays.asList(newVp[0], newVp[1], newVp[2])));
System.out.println("Old vp = " + vp);
System.out.println("New vp = " + Arrays.toString(result.get(0, 0)) + " " + Arrays.toString(result.get(1, 0)));
Point bary = new Point(frame.width() / 2, frame.height() / 2);
Mat homography = findHomography(vp, bary, frame.width(), frame.height());
lines = Lines.of(ransac.getBestDataSet().values());
lines = Lines.of(lines.perspectivTransform(homography));
Mat mask = new Mat(frame.size(), CvType.CV_8UC1, new Scalar(255));
Mat maskWarpped = new Mat();
Imgproc.warpPerspective(mask, maskWarpped, homography, frame.size());
Mat tmp = new Mat();
Imgproc.warpPerspective(frame, tmp, homography, frame.size(), Imgproc.INTER_LINEAR, Core.BORDER_REPLICATE, Scalar.all(255));
tmp.copyTo(dePerspectived, maskWarpped);
lines.draw(dePerspectived, new Scalar(0, 255, 0));
deskewedView.setImage(Tools.mat2jfxImage(dePerspectived));
} else
System.out.println("Not enough lines : " + lines.size());
} catch (Throwable e) {
e.printStackTrace();
}
}, 33, 250, TimeUnit.MILLISECONDS);
}
public static Mat calibrate(Mat uncalibrated) {
Mat dst = new Mat();
Core.gemm(Lines.K.inv(), uncalibrated, 1, new Mat(), 0, dst);
Core.normalize(dst, dst);
return dst;
}
public static Mat unCalibrate(Mat calibrated) {
Mat dst = new Mat();
Core.gemm(Lines.K, calibrated, 1, new Mat(), 0, dst);
if (dst.get(2, 0)[0] != 0) {
dst.put(0, 0, dst.get(0, 0)[0] / dst.get(2, 0)[0]);
dst.put(1, 0, dst.get(1, 0)[0] / dst.get(2, 0)[0]);
dst.put(2, 0, 1d);
}
return dst;
}
public Point[] rotate(Point bary, double alpha, Point... p) {
Mat matrix = Imgproc.getRotationMatrix2D(bary, alpha / Math.PI * 180, 1);
MatOfPoint2f results = new MatOfPoint2f();
Core.transform(new MatOfPoint2f(p), results, matrix);
return results.toArray();
}
private Mat findHomography(Point vp, Point bary, double width, double height) {
double alpha_ = Math.atan2((vp.y - bary.y), (vp.x - bary.x));
if (alpha_ < -Math.PI / 2 && alpha_ > -Math.PI)
alpha_ = alpha_ + Math.PI;
if (alpha_ < Math.PI && alpha_ > Math.PI / 2)
alpha_ = alpha_ - Math.PI;
double alpha = alpha_;
Point rotatedVp = rotate(bary, alpha, vp)[0];
Point A = new Point(0, 0);
Point B = new Point(width, 0);
Point C = new Point(width, height);
Point D = new Point(0, height);
Point AB2 = new Point(width / 2, 0);
Point CD2 = new Point(width / 2, height);
Point A_, B_, C_, D_;
if (rotatedVp.x >= width / 2) {
A_ = new Line(AB2, rotatedVp).intersection(0);
D_ = new Line(CD2, rotatedVp).intersection(0);
C_ = new Line(A_, bary).intersection(new Line(CD2, rotatedVp));
B_ = new Line(D_, bary).intersection(new Line(AB2, rotatedVp));
} else {
B_ = new Line(AB2, rotatedVp).intersection(width);
C_ = new Line(CD2, rotatedVp).intersection(width);
A_ = new Line(C_, bary).intersection(new Line(AB2, rotatedVp));
D_ = new Line(B_, bary).intersection(new Line(CD2, rotatedVp));
}
System.out.println("vp : " + vp);
System.out.println("rotated vp : " + rotatedVp);
System.out.println("Alpha : " + alpha * 180 / Math.PI);
// System.out.println("A : " + A + " " + A_);
// System.out.println("B : " + B + " " + B_);
// System.out.println("C : " + C + " " + C_);
// System.out.println("D : " + D + " " + D_);
return Imgproc.getPerspectiveTransform(new MatOfPoint2f(rotate(bary, -alpha, A_, B_, C_, D_)), new MatOfPoint2f(A, B, C, D));
}
public static class Lines extends org.genericsystem.cv.utils.Lines {
private static Mat K;
public Lines(Mat src) {
super(src);
}
public Lines(Collection<Line> lines) {
super(lines);
}
public static Lines of(Collection<Line> lines) {
return new Lines(lines);
}
public static void calibrate(Mat uncalibrate) {
Core.gemm(Lines.K.inv(), uncalibrate, 1, new Mat(), 0, uncalibrate);
Core.normalize(uncalibrate, uncalibrate);
}
public static Mat uncalibrate(Mat calibrated) {
Mat uncalibrate = new Mat(3, 1, CvType.CV_64FC1);
Core.gemm(Lines.K, calibrated, 1, new Mat(), 0, uncalibrate);
if (uncalibrate.get(2, 0)[0] != 0) {
uncalibrate.put(0, 0, uncalibrate.get(0, 0)[0] / uncalibrate.get(2, 0)[0]);
uncalibrate.put(1, 0, uncalibrate.get(1, 0)[0] / uncalibrate.get(2, 0)[0]);
uncalibrate.put(2, 0, 1);
}
return uncalibrate;
}
private static Mat getLineMat(Line line) {
Mat a = new Mat(3, 1, CvType.CV_64FC1);
Mat b = new Mat(3, 1, CvType.CV_64FC1);
a.put(0, 0, line.getX1());
a.put(1, 0, line.getY1());
a.put(2, 0, 1d);
b.put(0, 0, line.getX2());
b.put(1, 0, line.getY2());
b.put(2, 0, 1d);
Mat an = new Mat(3, 1, CvType.CV_64FC1);
Mat bn = new Mat(3, 1, CvType.CV_64FC1);
Core.gemm(K.inv(), a, 1, new Mat(), 0, an);
Core.gemm(K.inv(), b, 1, new Mat(), 0, bn);
Mat li = an.cross(bn);
Core.normalize(li, li);
a.release();
b.release();
an.release();
bn.release();
return li;
}
public Ransac<Line> vanishingPointRansac(double width, double height) {
int minimal_sample_set_dimension = 2;
double maxError = (float) 0.01623 * 2;
if (K == null) {
K = new Mat(3, 3, CvType.CV_64FC1, new Scalar(0));
K.put(0, 0, width);
K.put(0, 2, width / 2);
K.put(1, 1, height);
K.put(1, 2, height / 2);
K.put(2, 2, 1d);
}
return new Ransac<>(getLines(), getModelProvider(minimal_sample_set_dimension, maxError), minimal_sample_set_dimension, 100, maxError, Double.valueOf(Math.floor(this.size() * 0.7)).intValue());
}
private Function<Collection<Line>, Model<Line>> getModelProvider(int minimal_sample_set_dimension, double maxError) {
return datas -> {
Mat[] vp = new Mat[1];
if (datas.size() == minimal_sample_set_dimension) {
Iterator<Line> it = datas.iterator();
vp[0] = getLineMat(it.next()).cross(getLineMat(it.next()));
Core.normalize(vp[0], vp[0]);
} else {
// Extract the line segments corresponding to the indexes contained in the set
Mat li_set = new Mat(3, datas.size(), CvType.CV_64FC1);
Mat tau = new Mat(datas.size(), datas.size(), CvType.CV_64FC1, new Scalar(0, 0, 0));
int i = 0;
for (Line line : datas) {
Mat li = getLineMat(line);
li_set.put(0, i, li.get(0, 0));
li_set.put(1, i, li.get(1, 0));
li_set.put(2, i, li.get(2, 0));
tau.put(i, i, line.size());
i++;
}
// Least squares solution
// Generate the matrix ATA (from LSS_set=A)
Mat L = li_set.t();
Mat ATA = new Mat(3, 3, CvType.CV_64FC1);
Mat dst = new Mat();
Core.gemm(L.t(), tau.t(), 1, new Mat(), 0, dst);
Core.gemm(dst, tau, 1, new Mat(), 0, dst);
Core.gemm(dst, L, 1, new Mat(), 0, ATA);
// Obtain eigendecomposition
Mat v = new Mat();
Core.SVDecomp(ATA, new Mat(), v, new Mat());
// Check eigenvecs after SVDecomp
if (v.rows() < 3)
throw new IllegalStateException();
// Assign the result (the last column of v, corresponding to the eigenvector with lowest eigenvalue)
vp[0] = new Mat(3, 1, CvType.CV_64FC1);
vp[0].put(0, 0, v.get(0, 2));
vp[0].put(1, 0, v.get(1, 2));
vp[0].put(2, 0, v.get(2, 2));
Core.normalize(vp[0], vp[0]);
vp[0] = uncalibrate(vp[0]);
}
return new Model<Line>() {
@Override
public double computeError(Line line) {
Mat lineMat = getLineMat(line);
double di = vp[0].dot(lineMat);
di /= (Core.norm(vp[0]) * Core.norm(lineMat));
return di * di;
}
@Override
public double computeGlobalError(List<Line> datas, Collection<Line> consensusDatas) {
double globalError = 0;
for (Line line : datas) {
double error = computeError(line);
if (error > maxError)
error = maxError;
globalError += error;
}
globalError = globalError / datas.size();
return globalError;
}
@Override
public Object[] getParams() {
return new Object[] { vp[0] };
}
};
};
}
}
@Override
public void stop() throws Exception {
super.stop();
timer.shutdown();
timer.awaitTermination(5000, TimeUnit.MILLISECONDS);
capture.release();
}
}