Examples with ArrayImgFactory net.imglib2.img.array.ArrayImgFactory used on opensource projects

Example 21 with ArrayImgFactory

use of net.imglib2.img.array.ArrayImgFactory in project imagej-ops by imagej.

the class ConvolveTest method testConvolve.

/**
 * tests fft based convolve
 */
@Test
public void testConvolve() {
    float delta = 0.0001f;
    int[] size = new int[] { 225, 167 };
    int[] kernelSize = new int[] { 27, 39 };
    long[] borderSize = new long[] { 10, 10 };
    // create an input with a small sphere at the center
    Img<FloatType> in = new ArrayImgFactory<FloatType>().create(size, new FloatType());
    placeSphereInCenter(in);
    // create a kernel with a small sphere in the center
    Img<FloatType> kernel = new ArrayImgFactory<FloatType>().create(kernelSize, new FloatType());
    placeSphereInCenter(kernel);
    // create variables to hold the image sums
    FloatType inSum = new FloatType();
    FloatType kernelSum = new FloatType();
    FloatType outSum = new FloatType();
    FloatType outSum2 = new FloatType();
    FloatType outSum3 = new FloatType();
    // calculate sum of input and kernel
    ops.stats().sum(inSum, in);
    ops.stats().sum(kernelSum, kernel);
    // convolve and calculate the sum of output
    @SuppressWarnings("unchecked") final Img<FloatType> out = (Img<FloatType>) ops.run(ConvolveFFTF.class, in, kernel, borderSize);
    // create an output for the next test
    Img<FloatType> out2 = new ArrayImgFactory<FloatType>().create(size, new FloatType());
    // create an output for the next test
    Img<FloatType> out3 = new ArrayImgFactory<FloatType>().create(size, new FloatType());
    // Op used to pad the input
    final BinaryFunctionOp<RandomAccessibleInterval<FloatType>, Dimensions, RandomAccessibleInterval<FloatType>> padOp = (BinaryFunctionOp) Functions.binary(ops, PadInputFFTMethods.class, RandomAccessibleInterval.class, RandomAccessibleInterval.class, Dimensions.class, true);
    // Op used to pad the kernel
    final BinaryFunctionOp<RandomAccessibleInterval<FloatType>, Dimensions, RandomAccessibleInterval<FloatType>> padKernelOp = (BinaryFunctionOp) Functions.binary(ops, PadShiftKernelFFTMethods.class, RandomAccessibleInterval.class, RandomAccessibleInterval.class, Dimensions.class, true);
    // Op used to create the complex FFTs
    UnaryFunctionOp<Dimensions, RandomAccessibleInterval<ComplexFloatType>> createOp = (UnaryFunctionOp) Functions.unary(ops, CreateOutputFFTMethods.class, RandomAccessibleInterval.class, Dimensions.class, new ComplexFloatType(), true);
    final int numDimensions = in.numDimensions();
    // 1. Calculate desired extended size of the image
    final long[] paddedSize = new long[numDimensions];
    // if no getBorderSize() was passed in, then extend based on kernel size
    for (int d = 0; d < numDimensions; ++d) {
        paddedSize[d] = (int) in.dimension(d) + (int) kernel.dimension(d) - 1;
    }
    RandomAccessibleInterval<FloatType> paddedInput = padOp.calculate(in, new FinalDimensions(paddedSize));
    RandomAccessibleInterval<FloatType> paddedKernel = padKernelOp.calculate(kernel, new FinalDimensions(paddedSize));
    RandomAccessibleInterval<ComplexFloatType> fftImage = createOp.calculate(new FinalDimensions(paddedSize));
    RandomAccessibleInterval<ComplexFloatType> fftKernel = createOp.calculate(new FinalDimensions(paddedSize));
    // run convolve using the rai version with the memory created above
    ops.run(ConvolveFFTC.class, out2, paddedInput, paddedKernel, fftImage, fftKernel);
    ops.run(ConvolveFFTC.class, out3, paddedInput, paddedKernel, fftImage, fftKernel, true, false);
    ops.stats().sum(outSum, Views.iterable(out));
    ops.stats().sum(outSum2, out2);
    ops.stats().sum(outSum3, out3);
    // multiply input sum by kernelSum and assert it is the same as outSum
    inSum.mul(kernelSum);
    assertEquals(inSum.get(), outSum.get(), delta);
    assertEquals(inSum.get(), outSum2.get(), delta);
    assertEquals(inSum.get(), outSum3.get(), delta);
    assertEquals(size[0], out.dimension(0));
    assertEquals(size[0], out2.dimension(0));
}

Example 22 with ArrayImgFactory

use of net.imglib2.img.array.ArrayImgFactory in project imagej-ops by imagej.

the class ColocalisationTest method gaussianSmooth.

/**
 * Gaussian Smooth of the input image using intermediate float format.
 *
 * @param <T>
 * @param img
 * @param sigma
 * @return
 */
public static <T extends RealType<T> & NativeType<T>> Img<T> gaussianSmooth(RandomAccessibleInterval<T> img, double[] sigma) {
    Interval interval = Views.iterable(img);
    ImgFactory<T> outputFactory = new ArrayImgFactory<T>();
    final long[] dim = new long[img.numDimensions()];
    img.dimensions(dim);
    Img<T> output = outputFactory.create(dim, img.randomAccess().get().createVariable());
    final long[] pos = new long[img.numDimensions()];
    Arrays.fill(pos, 0);
    Localizable origin = new Point(pos);
    ImgFactory<FloatType> tempFactory = new ArrayImgFactory<FloatType>();
    RandomAccessible<T> input = Views.extendMirrorSingle(img);
    Gauss.inFloat(sigma, input, interval, output, origin, tempFactory);
    return output;
}

Example 23 with ArrayImgFactory

use of net.imglib2.img.array.ArrayImgFactory in project imagej-ops by imagej.

the class ColocalisationTest method produceMeanBasedNoiseImage.

/**
 * This method creates a noise image that has a specified mean. Every pixel
 * has a value uniformly distributed around mean with the maximum spread
 * specified.
 *
 * @return IllegalArgumentException if specified means and spreads are not
 *         valid
 */
public static <T extends RealType<T> & NativeType<T>> Img<T> produceMeanBasedNoiseImage(T type, int width, int height, double mean, double spread, double[] smoothingSigma, long seed) throws IllegalArgumentException {
    if (mean < spread || (mean + spread) > type.getMaxValue()) {
        throw new IllegalArgumentException("Mean must be larger than spread, and mean plus spread must be smaller than max of the type");
    }
    // create the new image
    ImgFactory<T> imgFactory = new ArrayImgFactory<T>();
    Img<T> noiseImage = imgFactory.create(new int[] { width, height }, // "Noise image");
    type);
    Random r = new Random(seed);
    for (T value : Views.iterable(noiseImage)) {
        value.setReal(mean + ((r.nextDouble() - 0.5) * spread));
    }
    // TO DO: call Ops
    return gaussianSmooth(noiseImage, smoothingSigma);
// filter.gaus instead
}

Example 24 with ArrayImgFactory

use of net.imglib2.img.array.ArrayImgFactory in project imagej-ops by imagej.

the class CreateImgTest method testImageFactory.

@Test
public void testImageFactory() {
    final Dimensions dim = new FinalDimensions(10, 10, 10);
    @SuppressWarnings("unchecked") final Img<DoubleType> arrayImg = (Img<DoubleType>) ops.run(CreateImgFromDimsAndType.class, dim, new DoubleType(), new ArrayImgFactory<DoubleType>());
    final Class<?> arrayFactoryClass = arrayImg.factory().getClass();
    assertEquals("Image Factory: ", ArrayImgFactory.class, arrayFactoryClass);
    @SuppressWarnings("unchecked") final Img<DoubleType> cellImg = (Img<DoubleType>) ops.run(CreateImgFromDimsAndType.class, dim, new DoubleType(), new CellImgFactory<DoubleType>());
    final Class<?> cellFactoryClass = cellImg.factory().getClass();
    assertEquals("Image Factory: ", CellImgFactory.class, cellFactoryClass);
}

Example 25 with ArrayImgFactory

use of net.imglib2.img.array.ArrayImgFactory in project imagej-ops by imagej.

the class CopyArrayImgTest method createData.

@Before
public void createData() {
    input = new ArrayImgFactory<UnsignedByteType>().create(new int[] { 120, 100 }, new UnsignedByteType());
    final Random r = new Random(System.currentTimeMillis());
    final Cursor<UnsignedByteType> inc = input.cursor();
    while (inc.hasNext()) {
        inc.next().setReal(r.nextDouble() * 255);
    }
}