#@ Dataset input
#@ OpService ops
#@ DatasetService ds
#@ ConvertService cs

import net.imagej.axis.Axes
import net.imglib2.IterableInterval
import net.imglib2.type.numeric.integer.UnsignedShortType
import ij.CompositeImage
import ij.ImagePlus
import ij.IJ

// For each channel, get the average of the two other channels

ch1 = ops.convert().uint16(ops.transform().hyperSliceView(input, input.dimensionIndex(Axes.CHANNEL), 0))
ch2 = ops.convert().uint16(ops.transform().hyperSliceView(input, input.dimensionIndex(Axes.CHANNEL), 1))
ch3 = ops.convert().uint16(ops.transform().hyperSliceView(input, input.dimensionIndex(Axes.CHANNEL), 2))

nCh1 = ops.math().add(ch2, ch3 as IterableInterval)
nCh2 = ops.math().add(ch1, ch3 as IterableInterval)
nCh3 = ops.math().add(ch1, ch2 as IterableInterval)

merged16 = ops.transform().stackView([nCh1, nCh2, nCh3])
divided = ops.create().img(merged16)

divideOp = ops.op("math.divide", nCh1.firstElement(), new UnsignedShortType(2))
ops.map(divided, merged16, divideOp)

merged = ops.create().img(input)
ops.image().invert(merged, ops.convert().uint8(divided))

// stuff needed for correct display in ImageJ1

result = ds.create(merged)
imp = cs.convert(result, ImagePlus.class)
resultImage = new CompositeImage(imp.duplicate(), IJ.COMPOSITE)
resultImage.setTitle(input.getName() + "_RGBinverted")
resultImage.show()