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CamLiveRetriever.java
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CamLiveRetriever.java
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package org.genericsystem.cv.classifier;
import java.lang.invoke.MethodHandles;
import java.util.ArrayList;
import java.util.Collection;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
import java.util.concurrent.ScheduledExecutorService;
import java.util.concurrent.ScheduledThreadPoolExecutor;
import java.util.concurrent.ThreadPoolExecutor;
import java.util.concurrent.TimeUnit;
import java.util.function.Predicate;
import java.util.stream.Collectors;
import javafx.scene.image.ImageView;
import javafx.scene.layout.GridPane;
import org.genericsystem.cv.AbstractApp;
import org.genericsystem.cv.Calibrated.AngleCalibrated;
import org.genericsystem.cv.Img;
import org.genericsystem.cv.lm.LMHostImpl;
import org.genericsystem.cv.utils.Line;
import org.genericsystem.cv.utils.NativeLibraryLoader;
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.MatOfPoint;
import org.opencv.core.MatOfPoint2f;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.imgproc.Imgproc;
import org.opencv.videoio.VideoCapture;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
@SuppressWarnings({ "resource" })
public class CamLiveRetriever extends AbstractApp {
static {
NativeLibraryLoader.load();
}
static final Logger logger = LoggerFactory.getLogger(MethodHandles.lookup().lookupClass());
private static long counter = 0;
private static final int STABILIZATION_DELAY = 500;
private static final int FRAME_DELAY = 100;
private final ScheduledExecutorService timerFields = new ScheduledThreadPoolExecutor(1, new ThreadPoolExecutor.DiscardPolicy());
private final VideoCapture capture = new VideoCapture(0);
private final Fields fields = new Fields();
private ImgDescriptor stabilizedImgDescriptor;
private Mat frame = new Mat();
private boolean stabilizationHasChanged = true;
private int stabilizationErrors = 0;
private Point vp = new Point(0, 0);
private AngleCalibrated calibrated;
public static void main(String[] args) {
launch(args);
}
@Override
public void stop() throws Exception {
super.stop();
timerFields.shutdown();
timerFields.awaitTermination(5, TimeUnit.SECONDS);
}
@Override
protected void fillGrid(GridPane mainGrid) {
capture.read(frame);
ImageView src0 = new ImageView(Tools.mat2jfxImage(frame));
mainGrid.add(src0, 0, 0);
ImageView src1 = new ImageView(Tools.mat2jfxImage(frame));
mainGrid.add(src1, 1, 0);
timerFields.scheduleAtFixedRate(() -> onSpace(), 0, STABILIZATION_DELAY, TimeUnit.MILLISECONDS);
AngleCalibrated.calibrate(frame.width(), frame.height());
calibrated = new AngleCalibrated(vp);
// Detect the rectangles
timerFields.scheduleAtFixedRate(() -> {
try {
Stats.beginTask("frame");
capture.read(frame);
if (frame == null) {
logger.warn("No frame !");
return;
}
Mat deperspectivGraphy = computeFrameToDeperspectivedHomography(frame);
if (deperspectivGraphy == null) {
logger.warn("Unable to compute a valid deperspectivation");
return;
}
if (stabilizationErrors > 20) {
// TODO: clean fields
fields.reset();
stabilizationErrors = 0;
stabilizedImgDescriptor = null;
}
if (stabilizedImgDescriptor == null) {
stabilizedImgDescriptor = new ImgDescriptor(frame, deperspectivGraphy);
return;
}
ImgDescriptor newImgDescriptor = new ImgDescriptor(frame, deperspectivGraphy);
Mat stabilizationHomography = stabilizedImgDescriptor.computeStabilizationGraphy(newImgDescriptor);
if (stabilizationHomography == null) {
stabilizationErrors++;
logger.warn("Unable to compute a valid stabilization ({} times)", stabilizationErrors);
return;
}
Img stabilized = warpPerspective(frame, stabilizationHomography);
Img stabilizedDisplay = new Img(stabilized.getSrc(), true);
if (stabilizationHasChanged) {
Stats.beginTask("stabilizationHasChanged");
Mat fieldsHomography = new Mat();
stabilized = newImgDescriptor.getDeperspectivedImg();
stabilizedDisplay = new Img(stabilized.getSrc(), true);
Core.gemm(deperspectivGraphy, stabilizationHomography.inv(), 1, new Mat(), 0, fieldsHomography);
Stats.beginTask("restabilizeFields");
fields.restabilizeFields(fieldsHomography);
Stats.endTask("restabilizeFields");
Stats.beginTask("merge fields");
fields.merge(detectRects(stabilizedDisplay));
Stats.endTask("merge fields");
fields.removeOverlaps();
final Img stabilizedDisplay_ = stabilizedDisplay;
fields.stream().filter(f -> f.getDeadCounter() == 0).forEach(f -> f.draw(stabilizedDisplay_, f.getDeadCounter() == 0 ? new Scalar(0, 255, 0) : new Scalar(0, 0, 255)));
stabilizedImgDescriptor = newImgDescriptor;
stabilizationHomography = deperspectivGraphy;
stabilizationHasChanged = false;
Stats.endTask("stabilizationHasChanged");
}
Img display = new Img(frame, false);
Stats.beginTask("consolidateOcr");
fields.performOcr(stabilized);
Stats.endTask("consolidateOcr");
fields.drawOcrPerspectiveInverse(display, stabilizationHomography.inv(), new Scalar(0, 255, 0), 1);
fields.drawIndestructible(display, stabilizationHomography.inv());
src0.setImage(display.toJfxImage());
src1.setImage(stabilizedDisplay.toJfxImage());
Stats.endTask("frame");
Stats.resetCumulative("RANSAC re-compute");
if (++counter % 10 == 0) {
System.out.println(Stats.getStatsAndReset());
counter = 0;
}
} catch (Throwable e) {
logger.warn("Exception while computing layout.", e);
}
}, 50, FRAME_DELAY, TimeUnit.MILLISECONDS);
}
@Override
protected void onSpace() {
stabilizationHasChanged = true;
}
private List<Rect> detectRects(Img stabilized) {
Img closed = stabilized.bilateralFilter(10, 80, 80).adaptativeGaussianInvThreshold(11, 3).morphologyEx(Imgproc.MORPH_CLOSE, Imgproc.MORPH_RECT, new Size(11, 3));
List<MatOfPoint> contours = new ArrayList<>();
Imgproc.findContours(closed.getSrc(), contours, new Mat(), Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
double minArea = 200;
List<Rect> res = contours.stream().filter(contour -> Imgproc.contourArea(contour) > minArea).map(c -> Imgproc.boundingRect(c)).collect(Collectors.toList());
res.forEach(rect -> Imgproc.rectangle(stabilized.getSrc(), rect.tl(), rect.br(), new Scalar(255, 200, 0)));
return res;
}
static Img warpPerspective(Mat frame, Mat homography) {
Mat dePerspectived = new Mat(frame.size(), CvType.CV_8UC3, Scalar.all(255));
Imgproc.warpPerspective(frame, dePerspectived, homography, frame.size(), Imgproc.INTER_LINEAR, Core.BORDER_REPLICATE, Scalar.all(255));
return new Img(dePerspectived, false);
}
private Lines houghlinesP(Mat frame) {
Img grad = new Img(frame, false).morphologyEx(Imgproc.MORPH_GRADIENT, Imgproc.MORPH_RECT, new Size(2, 2)).otsu().morphologyEx(Imgproc.MORPH_CLOSE, Imgproc.MORPH_ELLIPSE, new Size(3, 3));
return new Lines(grad.houghLinesP(1, Math.PI / 180, 10, 100, 10));
}
private Mat computeFrameToDeperspectivedHomography(Mat frame) {
Lines lines = houghlinesP(frame);
if (lines.size() > 10) {
lines = lines.reduce(20);
// lines.draw(frame, new Scalar(0, 0, 255));
lines = lines.filter(line -> distance(vp, line) < 0.48);
// lines.draw(frame, new Scalar(0, 255, 0));
lines = lines.reduce(10);
Stats.beginTask("ransac");
double[] newThetaPhi = new LMHostImpl<>((line, params) -> distance(new AngleCalibrated(params).uncalibrate(), line), lines.lines, calibrated.getTethaPhi()).getParams();
calibrated = calibrated.dump(newThetaPhi, 1);
vp = calibrated.uncalibrate();
Stats.endTask("ransac");
// System.out.println("Vanishing point : " + vp);
return findHomography(vp, frame.width(), frame.height());
} else {
System.out.println("Not enough lines : " + lines.size());
return null;
}
// Stats.beginTask("ransac");
// Ransac<Line> ransac = lines.vanishingPointRansac(frame.width(), frame.height());
// Stats.endTask("ransac");
// Mat vpMat = (Mat) ransac.getBestModel().getParams()[0];
// Point vp = new Point(vpMat.get(0, 0)[0], vpMat.get(1, 0)[0]);
// return findHomography(vp, frame.width(), frame.height());
}
private double distance(Point vp, Line line) {
double[] lineSegment = getNormalizedLine(line);
double n0 = -lineSegment[1];
double n1 = lineSegment[0];
double nNorm = Math.sqrt(n0 * n0 + n1 * n1);
double[] midPoint = getMiLine(line);
double r0, r1;
r0 = vp.y * midPoint[2] - midPoint[1];
r1 = midPoint[0] - vp.x * midPoint[2];
double rNorm = Math.sqrt(r0 * r0 + r1 * r1);
double num = (r0 * n0 + r1 * n1);
if (num < 0)
num = -num;
double d = 0;
if (nNorm != 0 && rNorm != 0)
d = num / (nNorm * rNorm);
// d *= line.size();
return d;
}
private double[] getNormalizedLine(Line line) {
double a = line.y1 - line.y2;
double b = line.x2 - line.x1;
double c = line.y1 * line.x2 - line.x1 * line.y2;
double norm = Math.sqrt(a * a + b * b + c * c);
return new double[] { a / norm, b / norm, c / norm };
}
private double[] getMiLine(Line line) {
return new double[] { (line.x1 + line.x2) / 2, (line.y1 + line.y2) / 2, 1d };
}
private Mat findHomography(Point vp, double width, double height) {
Point bary = new Point(width / 2, height / 2);
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();
// System.out.println("A : " + A + " " + A_);
// System.out.println("B : " + B + " " + B_);
// System.out.println("C : " + C + " " + C_);
// System.out.println("D : " + D + " " + D_);
Mat src = new MatOfPoint2f(rotate(bary, -alpha, A_, B_, C_, D_));
Mat dst = new MatOfPoint2f(A, B, C, D);
return Imgproc.getPerspectiveTransform(src, dst);
}
private Point[] rotate(Point bary, double alpha, Point... p) {
Mat matrix = Imgproc.getRotationMatrix2D(bary, alpha / Math.PI * 180, 1);
MatOfPoint2f points = new MatOfPoint2f(p);
MatOfPoint2f results = new MatOfPoint2f();
Core.transform(points, results, matrix);
return results.toArray();
}
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 Lines filter(Predicate<Line> predicate) {
return new Lines(lines.stream().filter(predicate).collect(Collectors.toList()));
}
public Lines reduce(int max) {
if (lines.size() <= max)
return this;
Set<Line> newLines = new HashSet<>();
while (newLines.size() < max)
newLines.add(lines.get((int) (Math.random() * size())));
return new Lines((newLines));
}
// private Mat getLineMat(Line line) {
// Mat a = new Mat(3, 1, CvType.CV_32F);
// Mat b = new Mat(3, 1, CvType.CV_32F);
// a.put(0, 0, new float[] { Double.valueOf(line.getX1()).floatValue() });
// a.put(1, 0, new float[] { Double.valueOf(line.getY1()).floatValue() });
// a.put(2, 0, new float[] { Double.valueOf(1d).floatValue() });
// b.put(0, 0, new float[] { Double.valueOf(line.getX2()).floatValue() });
// b.put(1, 0, new float[] { Double.valueOf(line.getY2()).floatValue() });
// b.put(2, 0, new float[] { Double.valueOf(1d).floatValue() });
// Mat an = new Mat(3, 1, CvType.CV_32F);
// Mat bn = new Mat(3, 1, CvType.CV_32F);
// 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);
// 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_32F, new Scalar(0));
// K.put(0, 0, new float[] { Double.valueOf(width).floatValue() });
// K.put(0, 2, new float[] { Double.valueOf(width / 2).floatValue() });
// K.put(1, 1, new float[] { Double.valueOf(height).floatValue() });
// K.put(1, 2, new float[] { Double.valueOf(height / 2).floatValue() });
// K.put(2, 2, new float[] { 1 });
// }
// 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;
//
// if (datas.size() == minimal_sample_set_dimension) {
// Iterator<Line> it = datas.iterator();
// vp = getLineMat(it.next()).cross(getLineMat(it.next()));
// Core.normalize(vp, vp);
// } else {
// Stats.beginCumulative("RANSAC re-compute");
// // Extract the line segments corresponding to the indexes contained in the set
// Mat li_set = new Mat(3, datas.size(), CvType.CV_32F);
// Mat tau = new Mat(datas.size(), datas.size(), CvType.CV_32F, 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_32F);
// 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 = new Mat(3, 1, CvType.CV_32F);
// vp.put(0, 0, v.get(0, 2));
// vp.put(1, 0, v.get(1, 2));
// vp.put(2, 0, v.get(2, 2));
//
// Core.normalize(vp, vp);
//
// Core.gemm(K, vp, 1, new Mat(), 0, vp);
//
// if (vp.get(2, 0)[0] != 0) {
// vp.put(0, 0, new float[] { Double.valueOf(vp.get(0, 0)[0] / vp.get(2, 0)[0]).floatValue() });
// vp.put(1, 0, new float[] { Double.valueOf(vp.get(1, 0)[0] / vp.get(2, 0)[0]).floatValue() });
// vp.put(2, 0, new float[] { 1 });
// } else {
// // Since this is infinite, it is better to leave it calibrated
// Core.gemm(K.inv(), vp, 1, new Mat(), 0, vp);
// }
// Stats.endCumulative("RANSAC re-compute");
// }
//
// return new Model<Line>() {
// @Override
// public double computeError(Line line) {
// Mat lineMat = getLineMat(line);
// double di = vp.dot(lineMat);
// di /= (Core.norm(vp) * 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 };
// }
//
// };
// };
// }
}
}