moved to new version number in preparation of V 1.0.9. Added access t…

…o fitted data in conrad.fitting akm
jenniferMaier · Mar 16, 2019 · 4ce59b7 · 4ce59b7
1 parent 47938ad
commit 4ce59b7
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Showing 9 changed files with 148 additions and 62 deletions.
diff --git a/src/edu/stanford/rsl/conrad/fitting/Function.java b/src/edu/stanford/rsl/conrad/fitting/Function.java
@@ -2,31 +2,53 @@
 
 import java.io.Serializable;
 
+import edu.stanford.rsl.jpop.FunctionOptimizer;
+import edu.stanford.rsl.jpop.OptimizableFunction;
+
 
 /**
  * Class to describe an abstract function that can be fiited to a set of 2D Points.
  * 
  * @author akmaier
  *
  */
-public abstract class Function implements Serializable {
+public abstract class Function implements Serializable, OptimizableFunction{
 
 	/**
 	 * 
 	 */
+	private boolean debug = false;
 	private static final long serialVersionUID = 5067981485975476424L;
 	protected boolean fittingDone = false;
 	protected int numberOfParameters = 0;
 	public abstract double [] getParametersAsDoubleArray();
-
+	public abstract void setParametersFromDoubleArray(double [] param);
+	private double fitdatax[];
+	private double fitdatay[];
 
 	/**
 	 * Fits the function to the given input data
 	 * @param x the input data
 	 * @param y the output data
 	 */
-	public abstract void fitToPoints(double [] x, double [] y);
+	public void fitToPoints(double [] x, double [] y){
+		fitdatax = x;
+		fitdatay = y;
+		FunctionOptimizer funcOpt = new FunctionOptimizer(this.getNumberOfParameters());
+		funcOpt.setInitialX(this.getInitialX());
+		funcOpt.optimizeFunction(this);
+		double [] solved = funcOpt.getOptimum();
+		this.setParametersFromDoubleArray(solved);
+		fittingDone = true;
+	};
 
+	protected double[] getInitialX() {
+		double init [] = new double [this.getNumberOfParameters()];
+		for (int i=0; i < this.getNumberOfParameters(); i++){
+			init[i] = 0;
+		}
+		return init;
+	}
 	public void fitToPoints(float []x, float []y){
 		double [] dx = new double[x.length];
 		double [] dy = new double[y.length];
@@ -61,4 +83,29 @@ public int getNumberOfParameters() {
 		return numberOfParameters;
 	}
 
+
+	@Override
+	public void setNumberOfProcessingBlocks(int number) {
+	}
+
+	@Override
+	public int getNumberOfProcessingBlocks() {
+		// TODO Auto-generated method stub
+		return 1;
+	}
+
+	@Override
+	public double evaluate(double[] x, int block) {
+		double value = 0;
+		this.setParametersFromDoubleArray(x);
+		for (int i=0; i < fitdatax.length; i++){
+			value += Math.pow(this.evaluate(fitdatax[i])- fitdatay[i], 2);
+		}
+		value = Math.sqrt(value);
+		// Catch parameterization errors and assign maximal cost.
+		if (!Double.isFinite(value)) value = Double.MAX_VALUE;
+		if (debug) System.out.println("Value " + value);
+		return value;
+	}
+
 }
diff --git a/src/edu/stanford/rsl/conrad/fitting/GaussianFunction.java b/src/edu/stanford/rsl/conrad/fitting/GaussianFunction.java
@@ -79,4 +79,10 @@ public double[] getParametersAsDoubleArray() {
 		return new double[]{mu, sigma};
 	}
 
+	@Override
+	public void setParametersFromDoubleArray(double[] param) {
+		mu = param[0];
+		sigma = param[1];
+	}
+
 }
diff --git a/src/edu/stanford/rsl/conrad/fitting/IdentityFunction.java b/src/edu/stanford/rsl/conrad/fitting/IdentityFunction.java
@@ -30,4 +30,9 @@ public double[] getParametersAsDoubleArray() {
 		return new double[] {};
 	}
 
+	@Override
+	public void setParametersFromDoubleArray(double[] param) {
+
+	}
+
 }
diff --git a/src/edu/stanford/rsl/conrad/fitting/LinearFunction.java b/src/edu/stanford/rsl/conrad/fitting/LinearFunction.java
@@ -98,4 +98,10 @@ public double[] getParametersAsDoubleArray() {
 		return new double[]{m,t};
 	}
 
+	@Override
+	public void setParametersFromDoubleArray(double[] param) {
+		m = param[0];
+		t = param[1];
+	}
+
 }
diff --git a/src/edu/stanford/rsl/conrad/fitting/LogarithmicFunction.java b/src/edu/stanford/rsl/conrad/fitting/LogarithmicFunction.java
@@ -1,10 +1,7 @@
 package edu.stanford.rsl.conrad.fitting;
 
-import java.util.Random;
 
-import edu.stanford.rsl.conrad.utils.DoubleArrayUtil;
-
-public class LogarithmicFunction extends Function{
+public class LogarithmicFunction extends Function {
 
 	/**
 	 * 
@@ -14,7 +11,8 @@ public class LogarithmicFunction extends Function{
 	private double b;
 	private double c;
 	private double d;
-
+
+
 	public LogarithmicFunction(){
 		numberOfParameters = 4;
 	}
@@ -43,7 +41,7 @@ private static double estimateD(double a, double b, double c, double x, double y
 
 	@Override
 	public double evaluate(double x){
-		return (a * Math.log((x - b) / c)) + d;
+		return (a * Math.log((x - b) / Math.abs(c))) + d;
 	}
 
 	private static double estimateB(double x1, double x2, double x3, double y1, double y2, double y3){
@@ -64,9 +62,9 @@ private static double estimateB(double x1, double x2, double x3, double y1, doub
 	public String toString(){
 		if (fittingDone){
 			return "y = (" + a + " * log((x - " +
-			b + ") / " +
-			c + ")) + " +
-			d;
+					b + ") / |" +
+					c + "|)) + " +
+					d;
 		} else {
 			return "y = (a * log((x - b) / c)) + d";
 		}
@@ -80,7 +78,7 @@ public void fitToThreePoints(double x1, double x2, double x3, double y1, double
 		fittingDone = true;
 	}
 
-	private boolean isValidEstimate(){
+	protected boolean isValidEstimate(){
 		boolean revan = true;
 		if (Double.isInfinite(a) || Double.isNaN(a)) revan = false;
 		if (Double.isInfinite(b) || Double.isNaN(b)) revan = false;
@@ -122,41 +120,18 @@ public void setD(double d) {
 		fittingDone = true;
 	}
 
+	@Override
+	protected double [] getInitialX(){
+		return new double []{1,1,1,0};
+	}
+
+
 	@Override
 	public void fitToPoints(double[] x, double [] y) {
 		if (x.length == 3) {
 			fitToThreePoints(x[0], x[1], x[2], y[0], y[1], y[2]);
 		} else {
-			Random random = new Random();
-			double [] stats = DoubleArrayUtil.minAndMaxOfArray(x);
-			double range = stats[1] - stats[0];
-			int tries = 20;
-			double a = 0;
-			double b = 0;
-			double c = 0;
-			double d = 0;
-			for (int i = 0; i < tries; i ++){
-				// select three random indices in the first the second and the third third
-				int fraction = 10;
-				int one = DoubleArrayUtil.findClosestIndex(stats[0] + (random.nextDouble() * (range / fraction)), x);
-				int two = DoubleArrayUtil.findClosestIndex(stats[0] + (range/2) +(random.nextDouble() * (range / fraction)), x);
-				int three = DoubleArrayUtil.findClosestIndex(stats[0] + range - (random.nextDouble() * (range / fraction)), x);
-				fitToThreePoints(x[one], x[two], x[three], y[one], y[two], y[three]);
-				if (isValidEstimate()){
-					a += this.a / tries;
-					b += this.b / tries;
-					c += this.c / tries;
-					d += this.d / tries;
-				} else {
-					// retry
-					i--;
-				}
-			}
-			this.a = a;
-			this.b = b;
-			this.c = c;
-			this.d = d;
-			fittingDone = true;
+			super.fitToPoints(x, y);
 		}
 	}
 
@@ -170,4 +145,25 @@ public double[] getParametersAsDoubleArray() {
 		return new double[]{a,b,c,d};
 	}
 
+	public static void main(String [] args){
+		LogarithmicFunction func = new LogarithmicFunction();
+		double [] x = {10000, 50000, 100000, 2000000, 600000000};
+		double [] y = {10, 20, 30, 500, 3000};
+		func.fitToPoints(x, y);
+		System.out.println(func 
+				+ "\n" + func.evaluate(10000)
+				+ "\n" + func.evaluate(50000)
+				+ "\n" + func.evaluate(2000000)
+				+ "\n" + func.evaluate(600000000)
+				+ "\n" + func.evaluate(83000000*1000));
+	}
+
+	@Override
+	public void setParametersFromDoubleArray(double[] param) {
+		this.a = param[0];
+		this.b = param[1];
+		this.c = param[2];
+		this.d = param[3];
+	}
+
 }
diff --git a/src/edu/stanford/rsl/conrad/fitting/PolynomialFunction.java b/src/edu/stanford/rsl/conrad/fitting/PolynomialFunction.java
@@ -69,7 +69,7 @@ public double evaluate(double x) {
 	public String toString() {
 		String revan = "" + params[0] + " ";
 		for (int i=1; i <= degree; i++){
-			revan += params[i] + " x^" +i;
+			revan += "+ " +params[i] + " x^" +i;
 		}
 		return revan;
 	}
@@ -79,4 +79,23 @@ public int getMinimumNumberOfCorrespondences() {
 		return degree + 1;
 	}
 
+	@Override
+	public void setParametersFromDoubleArray(double[] param) {
+		params = param;	
+	}
+
+	public static void main(String [] args){
+		PolynomialFunction func = new PolynomialFunction();
+		func.setDegree(5);
+		double [] x = {10000, 50000, 100000, 2000000, 600000000};
+		double [] y = {10, 50, 75, 500, 3000};
+		func.fitToPoints(x, y);
+		System.out.println(func 
+				+ "\n" + func.evaluate(10000)
+				+ "\n" + func.evaluate(50000)
+				+ "\n" + func.evaluate(2000000)
+				+ "\n" + func.evaluate(600000000)
+				+ "\n" + func.evaluate(83000000*1000));
+	}
+
 }
diff --git a/src/edu/stanford/rsl/conrad/fitting/RANSACFittedFunction.java b/src/edu/stanford/rsl/conrad/fitting/RANSACFittedFunction.java
@@ -145,4 +145,9 @@ public double[] getParametersAsDoubleArray() {
 		return baseFunction.getParametersAsDoubleArray();
 	}
 
+	@Override
+	public void setParametersFromDoubleArray(double[] param) {
+		baseFunction.setParametersFromDoubleArray(param);
+	}
+
 }
diff --git a/src/edu/stanford/rsl/conrad/utils/CONRAD.java b/src/edu/stanford/rsl/conrad/utils/CONRAD.java
@@ -21,7 +21,7 @@
 import edu.stanford.rsl.apps.gui.RawDataOpener;
 
 public abstract class CONRAD {
-	public static final String VersionString = "Version 1.0.8";
+	public static final String VersionString = "Version 1.0.9";
 	public static final String CONRADBibtex = "@article{Maier13-CSF," +
 			"  author = {A. Maier, H. G. Hofmann, M. Berger, P. Fischer, C. Schwemmer, H. Wu, K. Müller, J. Hornegger, J. H. Choi, C. Riess, A. Keil, and R. Fahrig},\n" +
 			"  title={{CONRAD - A software framework for cone-beam imaging in radiology}},\n" +

diff --git a/src/edu/stanford/rsl/tutorial/phantoms/FilePhantom.java b/src/edu/stanford/rsl/tutorial/phantoms/FilePhantom.java
@@ -1,5 +1,6 @@
 package edu.stanford.rsl.tutorial.phantoms;
 
+import ij.IJ;
 import ij.process.FloatProcessor;
 import ij.process.ImageProcessor;
 
@@ -13,31 +14,32 @@ public class FilePhantom extends Phantom {
 
 	public FilePhantom(int imgSzXGU, int imgSzYGU, String filename) {
 		super(imgSzXGU, imgSzYGU, filename);
+		FloatProcessor fip =null;
 		try {
 			ProjectionSource pSource = FileProjectionSource.openProjectionStream(filename);
 			Grid2D iProcessor = pSource.getNextProjection();
-			FloatProcessor fip = new FloatProcessor(iProcessor.getWidth(), iProcessor.getHeight(), iProcessor.getBuffer(), null);
-			if (iProcessor.getWidth()!= imgSzXGU || iProcessor.getHeight()!=imgSzYGU){
-				ImageProcessor resize = fip.resize(imgSzXGU, imgSzXGU);
-				for (int j=0;j<imgSzYGU; j++){
-					for (int i=0;i<imgSzXGU; i++){
-						setAtIndex(i, j, resize.getf(i, j));
-					}
+			fip =  new FloatProcessor(iProcessor.getWidth(), iProcessor.getHeight(), iProcessor.getBuffer(), null);
+
+		} catch (IOException e1) {
+			IJ.open(filename);
+			fip = IJ.getImage().getProcessor().toFloat(0, null);
+		}
+		if (fip.getWidth()!= imgSzXGU || fip.getHeight()!=imgSzYGU){
+			ImageProcessor resize = fip.resize(imgSzXGU, imgSzXGU);
+			for (int j=0;j<imgSzYGU; j++){
+				for (int i=0;i<imgSzXGU; i++){
+					setAtIndex(i, j, resize.getf(i, j));
 				}
 			}
-			else
-			{
-				for (int j=0;j<imgSzYGU; j++){
-					for (int i=0;i<imgSzXGU; i++){
-						setAtIndex(i, j, fip.getf(i,j));
-					}
+		}
+		else
+		{
+			for (int j=0;j<imgSzYGU; j++){
+				for (int i=0;i<imgSzXGU; i++){
+					setAtIndex(i, j, fip.getf(i,j));
 				}
 			}
-		} catch (IOException e1) {
-			e1.printStackTrace();
 		}
-
-
 	}
 
 }