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 * Interactive tutorial for SciJava Ops.
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package org.scijava.ops.tutorial;

import org.scijava.function.Computers;
import org.scijava.ops.api.OpEnvironment;
import org.scijava.ops.spi.OpCollection;
import org.scijava.ops.spi.OpMethod;

import net.imglib2.algorithm.neighborhood.Neighborhood;
import net.imglib2.algorithm.neighborhood.RectangleShape;
import net.imglib2.img.array.ArrayImgs;
import net.imglib2.type.numeric.integer.UnsignedByteType;

/**
 * SciJava Ops includes a mechanism for automatically introducing concurrency to
 * Ops. Developers can utilize this mechanism by writing their Ops on the
 * smallest element of the computation, be that a single pixel, or a
 * {@link Neighborhood}. SciJava Ops will then "lift" these Ops, creating
 * parallelized Ops that run on an entire
 * {@link net.imglib2.RandomAccessibleInterval} This tutorial showcases these
 * lifting mechanisms.
 *
 * @author Gabriel Selzer
 */
public class OpParallelization implements OpCollection {

	/**
	 * This Op, which is really just a computation on a single pixel, lets the
	 * framework assume the burden of parallelization
	 *
	 * @param input the input pixel
	 * @param output the preallocated output pixel (container)
	 * @implNote op names="tutorial.invertPerPixel"
	 */
	public static void invertOp(UnsignedByteType input, UnsignedByteType output) {
		output.set(255 - input.get());
	}

	/**
	 * This Op, which computes some algorithm over a neighborhood, also lets the
	 * framework assume the burden of parallelization
	 *
	 * @param input the input pixel
	 * @param output the preallocated output pixel (container)
	 * @implNote op names="tutorial.neighborhoodAverage"
	 */
	public static void averageNeighborhood(Neighborhood input,
		UnsignedByteType output)
	{
		long tmp = 0;
		var cursor = input.cursor();
		while (cursor.hasNext()) {
			tmp += cursor.next().getIntegerLong();
		}
		output.setInteger(tmp / input.size());
	}

	public static void main(String... args) {
		OpEnvironment ops = OpEnvironment.build();

		// First, we show parallelization at work for our per-pixel Op.
		// SciJava Ops understands how to apply that Op to each pixel of the input
		// image

		// Fill an input image with a value
		var fillValue = new UnsignedByteType(5);
		var inImg = ArrayImgs.unsignedBytes(10, 10);
		ops.op("image.fill").input(fillValue).output(inImg).compute();
		// Run the Op
		var outImg = ArrayImgs.unsignedBytes(10, 10);
		ops.op("tutorial.invertPerPixel").input(inImg).output(outImg).compute();
		// Get the original value, and the inverted value
		var original = inImg.firstElement().get();
		var inverted = outImg.firstElement().get();
		System.out.println("Original image was filled with value " + original +
			", and the inverted image is filled with value (255 - " + original +
			") = " + inverted);

		// Now, we show parallelization at work for our Parallelization Op.
		// For this example, we use a radius-1 rectangle; in other words, the
		// neighborhood
		// for a given pixel includes all of its immediate neighbors (including
		// diagonal)
		var shape = new RectangleShape(1, false);
		ops.op("tutorial.neighborhoodAverage").input(inImg, shape).output(outImg)
			.compute();
		// Get the original value, and the radius-1 neighborhood value
		original = inImg.firstElement().get();
		var mean = outImg.firstElement().get();
		System.out.println("Original image was filled with value " + original +
			", and the radius-1 mean at the corner is (4 * " + original + " / 9) = " +
			mean);
	}

}