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 * Image processing operations for SciJava Ops.
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package org.scijava.ops.image.filter.derivativeGauss;

import static org.junit.jupiter.api.Assertions.assertEquals;

import org.scijava.ops.image.AbstractOpTest;
import org.scijava.ops.image.util.TestImgGeneration;
import net.imglib2.Cursor;
import net.imglib2.FinalDimensions;
import net.imglib2.RandomAccess;
import net.imglib2.RandomAccessibleInterval;
import net.imglib2.img.Img;
import net.imglib2.type.numeric.real.DoubleType;
import net.imglib2.type.numeric.real.FloatType;

import org.junit.jupiter.api.Assertions;
import org.junit.jupiter.api.Test;
import org.scijava.types.Nil;

/**
 * Contains tests for {@link DefaultDerivativeGauss}.
 *
 * @author Gabriel Selzer
 */
public class DefaultDerivativeGaussTest extends AbstractOpTest {

	@Test // (expected = IllegalArgumentException.class)
	public void testImgParamDimensionsMismatch() {
		final RandomAccessibleInterval<FloatType> input = TestImgGeneration
			.floatArray(false, 30, 30, 30);

		final Img<DoubleType> output = ops.op("create.img").input(input).outType(
			new Nil<Img<DoubleType>>()
			{}).apply();

		final int[] derivatives = new int[] { 1, 0 };
		final double[] sigmas = new double[] { 1, 1 };
		IllegalArgumentException e = Assertions.assertThrows(
			IllegalArgumentException.class, () -> {
				ops.op("filter.derivativeGauss").input(input, sigmas, derivatives)
					.output(output).compute();
			});

		Assertions.assertTrue(e.getMessage().equalsIgnoreCase(
			"derivatives array must include values for each dimension!"));
	}

	@Test
	public void testDerivativeGauss() {
		final int width = 10;
		final Img<DoubleType> input = ops.op("create.img").input(
			new FinalDimensions(width, width), new DoubleType()).outType(
				new Nil<Img<DoubleType>>()
				{}).apply();

		final Img<DoubleType> output = ops.op("create.img").input(input,
			new DoubleType()).outType(new Nil<Img<DoubleType>>()
		{}).apply();

		// Draw a line on the image
		final RandomAccess<DoubleType> inputRA = input.randomAccess();
		inputRA.setPosition(5, 0);
		for (int i = 0; i < 10; i++) {
			inputRA.setPosition(i, 1);
			inputRA.get().set(255);
		}

		// filter the image
		final int[] derivatives = new int[] { 1, 0 };
		final double[] sigmas = new double[] { 0.5, 0.5 };
		ops.op("filter.derivativeGauss").input(input, sigmas, derivatives).output(
			output).compute();

		final Cursor<DoubleType> cursor = output.localizingCursor();
		int currentPixel = 0;
		while (cursor.hasNext()) {
			cursor.fwd();
			assertEquals(cursor.get().getRealDouble(),
				regressionRowValues[currentPixel % width], 0);
			currentPixel++;
		}
	}

	double[] regressionRowValues = { 0.0, 0.0, 0.0, 2.1876502452391353,
		117.25400606437196, 0.0, -117.25400606437196, -2.1876502452391353, 0.0,
		0.0 };

}
