Synthetic_Noise.Demo_Perlin_3d Maven / Gradle / Ivy
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ImageJ plugins related to chapters in previous book editions or other publications.
This code is part of the imagingbook software suite accompanying the
image processing textbooks by W. Burger and M.J. Burge (Springer 2006-2022).
The newest version!
/*******************************************************************************
* This software is provided as a supplement to the authors' textbooks on digital
* image processing published by Springer-Verlag in various languages and editions.
* Permission to use and distribute this software is granted under the BSD 2-Clause
* "Simplified" License (see http://opensource.org/licenses/BSD-2-Clause).
* Copyright (c) 2006-2022 Wilhelm Burger, Mark J. Burge.
* All rights reserved. Visit http://www.imagingbook.com for additional details.
*******************************************************************************/
package Synthetic_Noise;
import ij.IJ;
import ij.ImagePlus;
import ij.ImageStack;
import ij.plugin.PlugIn;
import ij.process.FloatProcessor;
import imagingbook.common.noise.hashing.HashFun;
import imagingbook.common.noise.perlin.PerlinNoiseGenNd;
/**
* This ImageJ plugin creates a new noise image using a 3D gradient noise
* generator. Several parameters can be adjusted. For details see
* Ch. 8 of W. Burger and M. J. Burge. "Principles of Digital Image Processing -
* Advanced Methods" (Vol. 3). Undergraduate Topics in Computer Science.
* Springer-Verlag, London (2013). https://www.imagingbook.com
*
* TODO: NOT WORKING! Needs to be fixed, results are not plausible!
*
* @author W. Burger
* @version 2013/05/28
*/
public class Demo_Perlin_3d implements PlugIn {
static int wx = 300;
static int wy = 300;
static int wz = 10; int stepZ = 5;
int dim = 3;
static int octaves = 6;
static int seed = 2;
static double f_min = 0.01; // f_max / (1 << (octaves-1));
static double f_max = f_min * (1 << (octaves-1));
static double persistence = 0.5;
String title = this.getClass().getSimpleName() + " Seed=" + seed;
public void run(String arg0) {
// choose hash function:
HashFun hf = HashFun.create(seed);
// HashFun hf = new Hash32Shift(seed);
// HashFun hf = new Hash32ShiftMult(seed);
// HashFun hf = new Hash32Ward(seed);
// HashFun hf = new HashPermute(seed);
// create the noise generator:
PerlinNoiseGenNd png = new PerlinNoiseGenNd(dim, f_min, f_max, persistence, hf);
ImageStack stack = createNoiseImage(wx, wy, wz, png);
ImagePlus im = new ImagePlus(this.getClass().getSimpleName(), stack);
im.setDisplayRange(-0.6,0.6);
im.show();
}
ImageStack createNoiseImage(int wx, int wy, int wz, PerlinNoiseGenNd ng) {
// ImagePlus stackImg =
// NewImage.createFloatImage(title, wx, wy, wz, NewImage.FILL_BLACK);
// ImageStack stack = stackImg.getStack();
ImageStack stack = new ImageStack(wx, wy);
double[] X = new double[dim];
IJ.showStatus("creating noise volume ...");
for (int z = 1; z <= wz; z++) {
IJ.showProgress(z, wz);
// ImageProcessor fp = stack.getProcessor(z);
FloatProcessor fp = new FloatProcessor(wx, wy);
for (int v = 0; v < wy; v++) {
for (int u = 0; u < wx; u++) {
X[0] = u;
X[1] = v;
X[2] = z * stepZ;
fp.setf(u, v, (float)ng.NOISE(X));
}
}
stack.addSlice(fp);
// fp.setMinAndMax(-0.6,0.6); //fp.resetMinAndMax();
}
IJ.showStatus("");
// stackImg.setDisplayRange(-0.6,0.6);
// return stackImg;
return stack;
}
}