A simple texture algorithm – faster code and more results

Faster Code

A while back I wrote about a simple texture algorithm that I have been exploring. The Python implementation was very slow – so much, that I decided to implement it in C++ to see what performance gain I would get. Surprisingly, the C++ version is about 100 faster, if not more. I expected a decent increase, but what once took several hours can now be done in a minute!

Not all of the performance gain is because of using C++ instead of Python. The Python version uses a data structure (an “enhanced” grid) that provides a lot of fancy syntax for operating on a grid. For instance:

The “enhanced” grid also provides several iterators that allow easy processing, for example:

• a cell iterator, that iterates over all values in the grid;
• an index iterator, that iterates over all possible tuples that can be used to index the grid; and
• a window iterator, that iterates over all cells in a rectangular portion of the grid.

The speed problem is caused by the implementation that supports the above features – an implementation that I thought was rather elegant. I knew there was a performance penalty, but after seeing to what extent I realise that I must have a hard look at how these features are implemented.

I am a big fan of late optimisation, especially for experimental code. But this incident has made me re-evaluate my position. After I had the fast C++ version running, I could do hordes of experiments very quickly – which meant I got a much better intuition of how parameters influence the resulting texture.

It also made me realise how important it is to check the performance of supporting data structures, even if they are merely convenience structures in non-production code (as is the “enhanced” grid).

More results

One of the things that struck me about the textures that this algorithm typically produces is the shapes of blotches of similar colour. I have been trying to enhance (and take advantage) these shapes by applying various filters on the texture; below are the results.

Original (generated from a random 5×5 matrix)	Dilation	Posterisation
Sobel edge detection	Median filter and Sobel edge detection	Median filter, differential edge detection
Emboss	Alien (Gimp filter)	Median filter.

The texture is also suitable for organic blend animations (compare with organic blending using Perlin noise).

Download

C++ Implementation

cpp_texture.zip (1.78 MB) (Visual Studio Solution)

The example uses PNG Writer , libpng and zlib (library files included with the download).