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ModularImageAnalysis (MIA) is an ImageJ plugin which provides a modular framework for assembling image and object analysis workflows. Detected objects can be transformed, filtered, measured and related. Analysis workflows are batch-enabled by default, allowing easy processing of high-content datasets.

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package io.github.mianalysis.mia.process.imagej;

import org.apache.commons.math3.exception.TooManyEvaluationsException;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresBuilder;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem;
import org.apache.commons.math3.fitting.leastsquares.LevenbergMarquardtOptimizer;
import org.apache.commons.math3.fitting.leastsquares.MultivariateJacobianFunction;
import org.apache.commons.math3.fitting.leastsquares.ParameterValidator;
import org.apache.commons.math3.geometry.euclidean.threed.Vector3D;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.RealVector;
import org.apache.commons.math3.util.Pair;

import ij.process.ImageProcessor;
import io.github.mianalysis.mia.process.math.GaussianDistribution2D;
import io.github.mianalysis.mia.process.math.Indexer;
import io.github.mianalysis.mia.process.math.Validator;

/**
 * Created by sc13967 on 05/06/2017.
 */
public class GaussianFitter {
//    public static void main(String[] args) {
//        new ImageJ();
//
//        ImagePlus iplReal = IJ.openImage("C:\\Users\\sc13967\\Desktop\\Crop.tif");
//        ImagePlus iplGauss = IJ.createImage("Gauss",iplReal.getWidth(),iplReal.getHeight(),1,8);
//        ImageProcessor iprReal = iplReal.getProcessor();
//
////        double x0, double y0, double sx, double sy, double A0, double ABG, double th
//        double[] pIn = new double[]{5.0,5.0,5.0,5.0,20.0,7.0,1.0};
//        double[][] limits = new double[][]{{0.0,11.0},
//                {0.0,11.0},{1.0E-50,1.7976931348623157E308},
//                {1.0E-50,1.7976931348623157E308},
//                {-1.7976931348623157E308,1.7976931348623157E308},
//                {-1.7976931348623157E308,1.7976931348623157E308},
//                {0.0,6.283185307179586}};
//        int maxEvaluations = 1000;
//
//        double[] output = fitGaussian2D(iprReal,pIn,limits,maxEvaluations);
////        double[] output = pIn;
//
//        if (output == null) {
//            System.err.println("No fit");
//            return;
//        }
//
//        GaussianDistribution2D fitDistribution2D = new GaussianDistribution2D(output[0],output[1],output[2],output[3],output[4],output[5],output[6]);
//
//        for (int xPos=0;xPos value(final RealVector p) {
        RealVector value = new ArrayRealVector(imagePoints.length);
        RealMatrix jacobian = new Array2DRowRealMatrix(imagePoints.length, 7);

        double x0 = p.getEntry(0); // centroid x
        double y0 = p.getEntry(1); // centroid y
        double sx = p.getEntry(2); // sigma x
        double sy = p.getEntry(3); // sigma y
        double A0 = p.getEntry(4); // peak amplitude
        double ABG = p.getEntry(5); // background amplitude
        double th = p.getEntry(6); // theta

        GaussianDistribution2D distribution2D = new GaussianDistribution2D(x0,y0,sx,sy,A0,ABG,th);

        for (int i=0;i(value, jacobian);

    }
};