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 * Image processing operations for SciJava Ops.
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package org.scijava.ops.image.features.haralick;

import java.util.function.Function;

import org.scijava.ops.image.image.cooccurrenceMatrix.MatrixOrientation;
import net.imglib2.RandomAccessibleInterval;
import net.imglib2.type.numeric.RealType;
import net.imglib2.type.numeric.real.DoubleType;

import org.scijava.ops.spi.OpDependency;

/**
 * Implementation of texture correlation haralick feature based on
 * http://earlglynn.github.io/RNotes/package/EBImage/Haralick-Textural-Features.html
 * .
 *
 * @author Andreas Graumann (University of Konstanz)
 * @author Christian Dietz (University of Konstanz)
 * @author Tim-Oliver Buchholz (University of Konstanz)
 * @implNote op names='features.haralick.correlation'
 */
public class DefaultCorrelation<T extends RealType<T>> extends
	AbstractHaralickFeature<T>
{

	// required functions
	@OpDependency(name = "features.haralick.coocMeanX")
	private Function<double[][], DoubleType> coocMeanXFunc;
	@OpDependency(name = "features.haralick.coocMeanY")
	private Function<double[][], DoubleType> coocMeanYFunc;
	@OpDependency(name = "features.haralick.coocStdY")
	private Function<double[][], DoubleType> coocStdYFunc;
	@OpDependency(name = "features.haralick.coocStdX")
	private Function<double[][], DoubleType> coocStdXFunc;

	/**
	 * TODO
	 *
	 * @param input
	 * @param numGreyLevels
	 * @param distance
	 * @param orientation
	 * @return the output
	 */
	@Override
	public DoubleType apply(final RandomAccessibleInterval<T> input,
		final Integer numGreyLevels, final Integer distance,
		final MatrixOrientation orientation)
	{
		final var matrix = getCooccurrenceMatrix(input, numGreyLevels,
			distance, orientation);

		final var nrGrayLevels = matrix.length;

		final var meanx = coocMeanXFunc.apply(matrix).get();
		final var meany = coocMeanYFunc.apply(matrix).get();
		final var stdx = coocStdXFunc.apply(matrix).get();
		final var stdy = coocStdYFunc.apply(matrix).get();

		double sum = 0;
		for (var i = 0; i < nrGrayLevels; i++) {
			for (var j = 0; j < nrGrayLevels; j++) {
				sum += i * j * matrix[i][j];
			}
		}

        var output = new DoubleType();
		output.set((sum - meanx * meany) / (stdx * stdy));
		return output;
	}

}
