/* * #%L * ImageJ software for multidimensional image processing and analysis. * %% * Copyright (C) 2014 - 2020 ImageJ developers. * %% * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions are met: * * 1. Redistributions of source code must retain the above copyright notice, * this list of conditions and the following disclaimer. * 2. Redistributions in binary form must reproduce the above copyright notice, * this list of conditions and the following disclaimer in the documentation * and/or other materials provided with the distribution. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. * #L% */ package net.imagej.ops.filter.convolve; import net.imagej.ops.Contingent; import net.imagej.ops.Ops; import net.imagej.ops.special.computer.AbstractUnaryComputerOp; import net.imglib2.Cursor; import net.imglib2.FinalInterval; import net.imglib2.RandomAccess; import net.imglib2.RandomAccessible; import net.imglib2.RandomAccessibleInterval; import net.imglib2.type.numeric.RealType; import net.imglib2.util.Intervals; import net.imglib2.view.Views; import org.scijava.plugin.Parameter; import org.scijava.plugin.Plugin; /** * Convolves an image naively. */ @Plugin(type = Ops.Filter.Convolve.class) public class ConvolveNaiveC<I extends RealType<I>, K extends RealType<K>, O extends RealType<O>> extends AbstractUnaryComputerOp<RandomAccessible<I>, RandomAccessibleInterval<O>> implements Ops.Filter.Convolve, Contingent { // TODO: should this be binary so we can use different kernels?? Not sure.. what if someone tried to re-use // with a big kernel that should be matched with ConvolveFFT @Parameter private RandomAccessibleInterval<K> kernel; @Override public void compute(final RandomAccessible<I> input, final RandomAccessibleInterval<O> output) { // TODO: try a decomposition of the kernel into n 1-dim kernels final long[] min = new long[input.numDimensions()]; final long[] max = new long[input.numDimensions()]; for (int d = 0; d < kernel.numDimensions(); d++) { min[d] = -kernel.dimension(d); max[d] = kernel.dimension(d) + output.dimension(d); } final RandomAccess<I> inRA = input.randomAccess(new FinalInterval(min, max)); final Cursor<K> kernelC = Views.iterable(kernel).localizingCursor(); final Cursor<O> outC = Views.iterable(output).localizingCursor(); final long[] pos = new long[input.numDimensions()]; final long[] kernelRadius = new long[kernel.numDimensions()]; for (int i = 0; i < kernelRadius.length; i++) { kernelRadius[i] = kernel.dimension(i) / 2; } float val; while (outC.hasNext()) { // image outC.fwd(); outC.localize(pos); // kernel inlined version of the method convolve val = 0; inRA.setPosition(pos); kernelC.reset(); while (kernelC.hasNext()) { kernelC.fwd(); for (int i = 0; i < kernelRadius.length; i++) { // dimension can have zero extension e.g. vertical 1d kernel if (kernelRadius[i] > 0) { inRA.setPosition(pos[i] + kernelC.getLongPosition(i) - kernelRadius[i], i); } } val += inRA.get().getRealDouble() * kernelC.get().getRealDouble(); } outC.get().setReal(val); } } @Override public boolean conforms() { // conforms only if the kernel is sufficiently small return Intervals.numElements(kernel) <= 9; } }