tag:blogger.com,1999:blog-6757805.post9059508625439784189..comments2023-12-24T07:08:44.996ZComments on Ken's blog: [zxevanas] Interpolated noiseUnknownnoreply@blogger.comBlogger1125tag:blogger.com,1999:blog-6757805.post-47383916436000627752012-11-20T21:33:59.550Z2012-11-20T21:33:59.550ZIf you like random images like that, you may enjoy...If you like random images like that, you may enjoy playing with power-law spectrums and FFTs, which can give you some fun textures. Here is some code to do this sort of thing in Python+NumPy+PIL (the numerical computation library with the fast Fourier transform and the imaging library): <a href="http://bpaste.net/show/opB2VHVB4HIpFyZUO0sq/" rel="nofollow">code</a>.<br /><br />Here's what that code produces: <a href="http://tmp.drostie.org/random.png" rel="nofollow">img</a>. You can get something closer to what you're trying for by changing (1 + r) to (1 + r)**2, or higher exponents (high frequency terms look more noisy) -- when it just goes like 1/r I believe it's what's called "brown noise", because I think when you go to a "power spectrum" you take the squared absolute value of the inverse FFT. You can also make the low frequencies more prominent by changing the 1 in the 1 + r to something else. :D<br />C.R. Drosthttp://drostie.org/noreply@blogger.com