使用1D perlin噪音创建粗略/不稳定的线条

Question

Could somebody help me get started with pseudo code or steps to use the 1D perlin noise to draw sketchy/wobbly lines as depicted in the page

http://freespace.virgin.net/hugo.elias/models/m_perlin.htm

_{(source: virgin.net )}

I did find that aforge.net has functions for 1D and 2D perlin noise functions. It shows an example ofusing the 2D noise to generate clouds.

http://www.aforgenet.com/framework/docs/html/f2f36ce1-ddab-389e-b538-0ccaca81fa87.htm

But am not sure how to go about using it to generate wobbly lines.

Answer 1

Only difference you need to make going from 1D to 2D is to calculate the slope. Once you have the slope and the 1D noise, just add that noise in the direction perpendicular to the direction of slope.

Answer 2

I saw your question hoping for pseudocode as an answer. I ended up not using the gradient (I already have a simplex noise function that would be difficult to make compatible) but doing something else.

The idea is to choose an interval length (unity is fine, but larger ones will yield less rounding error) and using a random seed consider the noise interval [s, s + I] where I is the interval length and s is the seed. You then "wrap" it around a circle by mapping an angle theta to I / (2 * Pi) * theta . However, you need to make sure that the noise values of s and s + I are the same, else you would have a discontinuity in the circle. This is easy, however; given x in [s, s + I] , just do this:

interpolate(x, s, I)
  mid <- noise(s) - noise(s + I)
  out <- noise(x)
  out <- out + (x - s) / I * mid
  out <- out - (s + I - x) / I * mid
  return out

Note that if x = s , we subtract mid , and if x = s + I , we add mid .

We now have a mapping from an angle to a noise value. Given the radius we wish our circle to have, we can set the minimum and maximum values of the noise then, for any theta we wish to evaluate, just add the corresponding value to the radius of the circle. In polar coordinates, this looks like:

radius(theta)
  s <- randomDouble
  I <- 1
  theta <- theta / (2 * pi)
  dr <- interpolate(theta, s, I)
  dr <- dr * (noisemax - noisemin) / 2 + (noisemax + noisemin) / 2
  return dr + r

assuming you already have your average radius ( r ) and the variance you want ( noisemin , noisemax ), and that your noise function outputs values in [-1, 1] . You will want a tight spread between noisemax and noisemin, else your circle will be mostly wobble.

For a square the process it the same but you do not need any controls or interpolation, just the scaling factors and an interval square side length.

You can get fancy and do a nonlinear interpolation but I don't really see the need, and it might mess up the simplex noise.