generalbgh/algorithms-for-computing-line-estimators
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algorithms-for-computing-line-estimators
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executable jar funktioniert endlich mit opencv-2413

This commit is contained in:
Armin Wolf 2017-10-12 17:21:16 +02:00
parent bf0d6267cb
commit 1ee749cc08
15 changed files with 169 additions and 61 deletions
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LinearRegressionTool/libs/opencv-2413.jar
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LinearRegressionTool/libs/x64/opencv_java2413.dll
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28
LinearRegressionTool/pom.xml
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@ -97,26 +97,6 @@
<version>1.6.11</version>
</dependency>
<!-- OpenCV -->
<dependency>
<groupId>org</groupId>
<artifactId>opencv</artifactId>
<version>2.4.13</version>
<scope>system</scope>
<systemPath>${project.basedir}/libs/opencv-2413.jar</systemPath>
<classifier>native-all</classifier>
</dependency>
<dependency>
<groupId>org</groupId>
<artifactId>opencv</artifactId>
<version>2.4.13</version>
<scope>system</scope>
<type>dll</type>
<systemPath>${project.basedir}/libs/x64/opencv_java2413.dll</systemPath>
<classifier>native-all</classifier>
</dependency>
<!-- https://mvnrepository.com/artifact/commons-io/commons-io -->
<dependency>
<groupId>commons-io</groupId>
@ -124,6 +104,12 @@
<version>2.5</version>
</dependency>
<dependency>
<groupId>org.openpnp</groupId>
<artifactId>opencv</artifactId>
<version>3.2.0-1</version>
</dependency>
</dependencies>
</project>

68
LinearRegressionTool/src/main/java/App.java
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@ -1,13 +1,21 @@
import model.Line;
import model.LineModel;
import nu.pattern.OpenCV;
import org.apache.commons.io.IOUtils;
import org.opencv.core.Core;
import presenter.Presenter;
import presenter.algorithms.advanced.RepeatedMedianEstimator;
import presenter.generator.DatasetGenerator;
import view.MainFrame;
import javax.swing.*;
import java.awt.*;
import java.io.*;
import java.io.File;
import java.io.FileOutputStream;
import java.io.InputStream;
import java.io.OutputStream;
import java.util.LinkedList;
import static nu.pattern.OpenCV.loadShared;
/**
* Implementierung verschiedener Algorithmen zur Berechnung von Ausgleichsgeraden.
@ -67,15 +75,23 @@ public class App {
SwingUtilities.updateComponentTreeUI(view);
}
static {
nu.pattern.OpenCV.loadShared();
System.loadLibrary(org.opencv.core.Core.NATIVE_LIBRARY_NAME);
}
public static void main(String[] args) {
//loadLibrary();
System.out.println("Welcome to OpenCV " + Core.VERSION);
System.out.println(System.getProperty("java.library.path"));
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
final String[] options = {"Normal", "Benchmark"};
String chosen = (String) JOptionPane.showInputDialog(null,
"Bitte wählen Sie eine Ansicht!",
"Ausführungsoptionen",
JOptionPane.QUESTION_MESSAGE,
null,
options,
options[0]);
if (chosen == "Normal"){
final Presenter presenter = new Presenter(new LineModel(), null);
SwingUtilities.invokeLater(() -> {
JFrame.setDefaultLookAndFeelDecorated(true);
MainFrame view = new MainFrame();
@ -84,8 +100,40 @@ public class App {
view.setPresenter(presenter);
view.setActionListeners();
presenter.setView(view);
});
});} else {
benchmark();
}
}
public static void benchmark() {
int[] sizes = {50, 500, 1000, 3000, 5000, 10000};
for (int i = 5; i < sizes.length; i++) {
System.out.println("---- Datasetgröße: " + sizes[i] + " ----");
DatasetGenerator generator = new DatasetGenerator();
LinkedList<Line> lines = generator.generateDataCloud(sizes[i]);
// IntersectionComputer computer = new IntersectionComputer(lines);
// ArrayList<Point> points = computer.compute();
//algorithms
//NaivLeastMedianOfSquaresEstimator naivLms = new NaivLeastMedianOfSquaresEstimator(lines);
//naivLms.run();
// NaivRepeatedMedianEstimator naivRm = new NaivRepeatedMedianEstimator(lines);
// naivRm.run();
//
// NaivTheilSenEstimator naivTs = new NaivTheilSenEstimator(lines);
// naivTs.run();
// LeastMedianOfSquaresEstimator lms = new LeastMedianOfSquaresEstimator(lines,points);
// lms.run();
RepeatedMedianEstimator rm = new RepeatedMedianEstimator(lines);
rm.run();
// TheilSenEstimator ts = new TheilSenEstimator(lines,points);
// ts.run();
}
}
}

4
LinearRegressionTool/src/main/java/presenter/Presenter.java
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@ -196,12 +196,12 @@ public class Presenter extends AbstractPresenter {
}
}
public void generateDataset() {
public void generateDataset(int n) {
if (generatorThread == null || !generatorThread.isAlive()) {
generatorThread = new Thread(() -> {
DatasetGenerator generator = new DatasetGenerator();
generator.addObserver(this);
getModel().setLines(generator.generateCircle(100));
getModel().setLines(generator.generateDataCloud(n));
calculateIntersections();
getView().enableFunctionality();
});

11
LinearRegressionTool/src/main/java/presenter/algorithms/advanced/LeastMedianOfSquaresEstimator.java
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@ -64,6 +64,11 @@ public class LeastMedianOfSquaresEstimator extends Observable implements Algorit
*/
public void run() {
System.out.println("=== S T A R T - L M S ===");
long start;
long end;
start = System.currentTimeMillis();
//(2.) Let U <- (-inf, inf) be the initial active intervals...
Comparator<Interval> comparator = (o1, o2) -> {
if (o1.getDistance() < o2.getDistance()) {
@ -91,7 +96,7 @@ public class LeastMedianOfSquaresEstimator extends Observable implements Algorit
//(b.) apply plane sweep
if ((constant * n) >= numberOfIntersections) {
sigmaMin = planeSweep(interval);
sigmaMin = pseudoSweep(interval);
} else {
//(c.) otherwise....
// get random intersections point...
@ -131,6 +136,8 @@ public class LeastMedianOfSquaresEstimator extends Observable implements Algorit
this.intervals.remove(interval);
}
}
end = System.currentTimeMillis();
System.out.println("Zeit: "+ ((end-start)/1000));
}
/**
@ -154,7 +161,7 @@ public class LeastMedianOfSquaresEstimator extends Observable implements Algorit
* @param interval
* @return
*/
public Line planeSweep(Interval interval) {
public Line pseudoSweep(Interval interval) {
//initialisiere die x-Queue mit den 2D Punkten und sortiere nach x-Lexikographischer Ordnung
ArrayList<Point> xQueue = new ArrayList<>();

7
LinearRegressionTool/src/main/java/presenter/algorithms/advanced/RepeatedMedianEstimator.java
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@ -87,6 +87,11 @@ public class RepeatedMedianEstimator extends Observable implements Algorithm {
*/
public void run() {
System.out.println("=== S T A R T - R M ===");
long start;
long end;
start = System.currentTimeMillis();
while (linesInCenterSlab.size() != 1) {
n = linesInCenterSlab.size();
r = Math.ceil(Math.pow(n, beta));
@ -124,6 +129,8 @@ public class RepeatedMedianEstimator extends Observable implements Algorithm {
contractIntervals();
}
end = System.currentTimeMillis();
System.out.println("Zeit: "+ ((end-start)/1000));
}
/**

8
LinearRegressionTool/src/main/java/presenter/algorithms/naiv/NaivLeastMedianOfSquaresEstimator.java
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@ -37,8 +37,11 @@ public class NaivLeastMedianOfSquaresEstimator implements Algorithm {
Double beta;
Double alpha;
Double dijk;
System.out.println("=== S T A R T - naiv L M S ===");
long start;
long end;
start = System.currentTimeMillis();
for (Point i : set) {
System.out.println(i);
for (Point j : set) {
for (Point k : set) {
triple.add(i);
@ -58,7 +61,8 @@ public class NaivLeastMedianOfSquaresEstimator implements Algorithm {
}
}
}
System.out.printf("Naiv LMS: %6.2f * x + %6.3f\n", m, b);
end = System.currentTimeMillis();
System.out.println("Zeit: "+ ((end-start)/1000));
}

7
LinearRegressionTool/src/main/java/presenter/algorithms/naiv/NaivRepeatedMedianEstimator.java
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@ -36,6 +36,10 @@ public class NaivRepeatedMedianEstimator implements Algorithm {
@Override
public void run() {
//init the List for the slopes
System.out.println("=== S T A R T - naiv R M ===");
long start;
long end;
start = System.currentTimeMillis();
for (int j = 0; j < lines.size(); j++) {
Line leq = lines.get(j);
if (slopesPerLine.get(leq.getId()) == null) {
@ -79,7 +83,8 @@ public class NaivRepeatedMedianEstimator implements Algorithm {
medianX = FastElementSelector.randomizedSelect(xMedians, xMedians.size() / 2);
medianY = FastElementSelector.randomizedSelect(yMedians, yMedians.size() / 2);
System.out.printf("Naiv RM: %6.2f * x + %6.2f \n", medianX, medianY);
end = System.currentTimeMillis();
System.out.println("Zeit: "+ ((end-start)/1000));
}
@Override

7
LinearRegressionTool/src/main/java/presenter/algorithms/naiv/NaivTheilSenEstimator.java
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@ -30,6 +30,10 @@ public class NaivTheilSenEstimator implements Algorithm {
@Override
public void run() {
System.out.println("=== S T A R T - naiv T S ===");
long start;
long end;
start = System.currentTimeMillis();
ArrayList<Double> slopesList = new ArrayList<>();
int cnt = 0;
for (int i = 0; i < lines.size(); i++) {
@ -56,7 +60,8 @@ public class NaivTheilSenEstimator implements Algorithm {
Double median2 = FastElementSelector.randomizedSelect(list2, list2.size() / 2);
slope = FastElementSelector.randomizedSelect(slopesList, slopesList.size() / 2);
yInterception = median2 - slope * median1;
System.out.printf("Naiv TS: %6.2f * x + %6.3f\n", slope, yInterception);
end = System.currentTimeMillis();
System.out.println("Zeit: "+ ((end-start)/1000));
}
@Override

20
LinearRegressionTool/src/main/java/presenter/algorithms/util/IntersectionComputer.java
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@ -41,23 +41,23 @@ public class IntersectionComputer {
yMaximum = Double.MIN_VALUE;
}
public ArrayList<Point> compute() {
public ArrayList<Point> compute(double lower, double higher) {
if (lines.size() >= 12) {
worker[0] = new Thread(() -> {
work(lines.subList(0, (lines.size() / 4)));
work(lines.subList(0, (lines.size() / 4)),lower,higher);
});
worker[0].start();
worker[1] = new Thread(() -> {
work(lines.subList((lines.size() / 4) + 1, (lines.size() / 2)));
work(lines.subList((lines.size() / 4) + 1, (lines.size() / 2)),lower,higher);
});
worker[1].start();
worker[2] = new Thread(() -> {
work(lines.subList((lines.size() / 2) + 1, 3 * (lines.size() / 4)));
work(lines.subList((lines.size() / 2) + 1, 3 * (lines.size() / 4)),lower,higher);
});
worker[2].start();
worker[3] = new Thread(() -> {
work(lines.subList(3 * (lines.size() / 4) + 1, (lines.size())));
work(lines.subList(3 * (lines.size() / 4) + 1, (lines.size())),lower,higher);
});
worker[3].start();
@ -70,16 +70,20 @@ public class IntersectionComputer {
}
}
} else {
work(lines);
work(lines,lower,higher);
}
return new ArrayList<>(intersections);
}
private synchronized void work(List<Line> lines) {
public ArrayList<Point> compute() {
return compute(-99999, 99999);
}
private synchronized void work(List<Line> lines, double lower, double higher) {
IntersectionCounter counter = new IntersectionCounter();
counter.run(lines, new Interval(-99999, 99999));
counter.run(lines, new Interval(lower, higher));
ArrayList<Point> points = counter.calculateIntersectionAbscissas();
for (Point p : points) {
if (!isFound(p)) {

16
LinearRegressionTool/src/main/java/presenter/evaluation/EvaluateAlgorithms.java
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@ -129,10 +129,6 @@ public class EvaluateAlgorithms extends Observable {
ArrayList<String> result;
ArrayList<ArrayList<String>> multipleResults = new ArrayList<>();
startLMS();
startRM();
startTS();
System.out.println("alle advanced algorithmen sind berechnet!");
switch (type) {
case 0:
//der alg der gewählt wurde
@ -191,6 +187,8 @@ public class EvaluateAlgorithms extends Observable {
ArrayList<Double[]> lineRes;
switch (alg) {
case 3:
startLMS();
startRM();
result = getPercentigeErrorBasedMeasure(arrangement.getLines(), lmsRes[0], lmsRes[1]);
multipleResults.add(result);
@ -206,6 +204,9 @@ public class EvaluateAlgorithms extends Observable {
break;
case 4:
startLMS();
startTS();
result = getPercentigeErrorBasedMeasure(arrangement.getLines(), lmsRes[0], lmsRes[1]);
multipleResults.add(result);
result = fillPseudoResults();
@ -219,6 +220,9 @@ public class EvaluateAlgorithms extends Observable {
sendPloteLineResults(lineRes, new Integer[]{0, 2});
break;
case 5:
startRM();
startTS();
result = fillPseudoResults();
multipleResults.add(result);
result = getPercentigeErrorBasedMeasure(arrangement.getLines(), rmRes[0], rmRes[1]);
@ -232,6 +236,10 @@ public class EvaluateAlgorithms extends Observable {
sendPloteLineResults(lineRes, new Integer[]{1, 2});
break;
case 6:
startLMS();
startRM();
startTS();
result = getPercentigeErrorBasedMeasure(arrangement.getLines(), lmsRes[0], lmsRes[1]);
multipleResults.add(result);
result = getPercentigeErrorBasedMeasure(arrangement.getLines(), rmRes[0], rmRes[1]);

13
LinearRegressionTool/src/main/java/presenter/evaluation/PictureProcessor.java
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@ -1,8 +1,9 @@
package presenter.evaluation;
import model.Line;
import org.opencv.core.*;
import org.opencv.highgui.Highgui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import presenter.Presenter;
@ -35,7 +36,7 @@ public class PictureProcessor extends Observable {
}
public void run() {
image = Highgui.imread(file.getAbsolutePath());
image = Imgcodecs.imread(file.getAbsolutePath());
contour = process(image);
contourLength = image.width() * 0.3;
createInputData();
@ -44,12 +45,12 @@ public class PictureProcessor extends Observable {
private Mat process(Mat image) {
Mat threshold = new Mat(image.width(), image.height(), CvType.CV_8UC1);
Mat source = new Mat(image.width(), image.height(), CvType.CV_8UC1);
Highgui.imwrite(file.getAbsolutePath().substring(0,file.getAbsolutePath().lastIndexOf("."))+"-orig.png", image);
Imgcodecs.imwrite(file.getAbsolutePath().substring(0,file.getAbsolutePath().lastIndexOf("."))+"-orig.png", image);
Imgproc.cvtColor(image, source, Imgproc.COLOR_BGR2GRAY);
Highgui.imwrite(file.getAbsolutePath().substring(0,file.getAbsolutePath().lastIndexOf("."))+"-Greyscale.png", source);
Imgcodecs.imwrite(file.getAbsolutePath().substring(0,file.getAbsolutePath().lastIndexOf("."))+"-Greyscale.png", source);
Imgproc.GaussianBlur(source, threshold, new Size(3, 3), 0, 0);
Imgproc.Canny(threshold, source, 300, 600, 5, true);
Highgui.imwrite(file.getAbsolutePath().substring(0,file.getAbsolutePath().lastIndexOf("."))+"-Canny.png", source);
Imgcodecs.imwrite(file.getAbsolutePath().substring(0,file.getAbsolutePath().lastIndexOf("."))+"-Canny.png", source);
//Konturen berechnen und filtern
contours = new ArrayList<>();
Imgproc.findContours(source, contours, new Mat(), Imgproc.RETR_TREE, Imgproc.CHAIN_APPROX_SIMPLE, new Point(0, 0));
@ -62,7 +63,7 @@ public class PictureProcessor extends Observable {
Imgproc.drawContours(viscont, contours, i, new Scalar(255, 0, 0), 1);
}
}
Highgui.imwrite(file.getAbsolutePath().substring(0,file.getAbsolutePath().lastIndexOf("."))+"-Contour.png", viscont);
Imgcodecs.imwrite(file.getAbsolutePath().substring(0,file.getAbsolutePath().lastIndexOf("."))+"-Contour.png", viscont);
Mat resized = new Mat();
Imgproc.resize(viscont, resized, new Size(560, 560));
SwingUtilities.invokeLater(() -> {

4
LinearRegressionTool/src/main/java/presenter/generator/DatasetGenerator.java
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@ -47,7 +47,7 @@ public class DatasetGenerator extends Observable {
line.setId(i - 1 + "");
lines.add(line);
}
String[] ret = {"generator", "Es wurden " + 100 + " Daten generiert mit den Parametern",
String[] ret = {"generator", "Es wurden " + size + " Daten generiert mit den Parametern",
"</br> <b>m</b> = " + m + "",
"</br> <b>b</b> = " + b + ""};
setChanged();
@ -89,7 +89,7 @@ public class DatasetGenerator extends Observable {
idx++;
}
String[] ret = {"generator", "Es wurden " + 100 + " Daten generiert mit den Parametern"};
String[] ret = {"generator", "Es wurden " + n + " Daten generiert mit den Parametern"};
setChanged();
notifyObservers(ret);
return lines;

37
LinearRegressionTool/src/main/java/view/listener/GenerateDataListener.java
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@ -2,8 +2,13 @@ package view.listener;
import presenter.Presenter;
import javax.swing.*;
import java.awt.*;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import java.text.DecimalFormat;
import java.text.NumberFormat;
import java.util.Locale;
/**
@ -16,6 +21,7 @@ import java.awt.event.ActionListener;
public class GenerateDataListener implements ActionListener {
private Presenter presenter;
private JDialog dialog;
public GenerateDataListener(Presenter presenter) {
this.presenter = presenter;
@ -23,7 +29,34 @@ public class GenerateDataListener implements ActionListener {
@Override
public void actionPerformed(ActionEvent e) {
Thread t = new Thread(() -> presenter.generateDataset());
t.start();
SwingUtilities.invokeLater(() -> {
if (dialog == null || !dialog.isVisible()) {
dialog = new JDialog();
dialog.setAlwaysOnTop(true);
NumberFormat numberFormat = NumberFormat.getNumberInstance(Locale.getDefault());
DecimalFormat decimalFormat = (DecimalFormat) numberFormat;
decimalFormat.setGroupingUsed(false);
JTextField textField = new JFormattedTextField(decimalFormat);
textField.setColumns(20);
JButton aproveButton = new JButton("start");
dialog.setSize(350, 70);
dialog.setLayout(new FlowLayout());
dialog.setResizable(false);
dialog.setLocationRelativeTo(presenter.getView());
dialog.add(textField);
dialog.add(aproveButton);
aproveButton.addActionListener(e1 -> {
int n = Integer.parseInt(textField.getText());
Thread t = new Thread(() -> presenter.generateDataset(n));
t.start();
dialog.dispose();
});
dialog.setVisible(true);
}
});
}
}