A Simple Procedural Texture Algorithm


I am playing around with generating textures and decided to post some preliminary results. The algorithm used to create these images is simple to implement, but slow. Here is how it works:

1. Generate White Noise

Start off with white noise (grayscale only – colour is much too slow).

2. Blend with random neighbourhood pixels

Generate a new image from the old one. Every pixel in the new image is a blend between the corresponding pixel in the other image, and a randomly selected pixel in a square window around that pixel. Every point in that window can be selected with a probability that is defined in a square matrix.

This matrix determines how the texture will turn out; it is unfortunately a bit hard to guess how the texture will look given the matrix, in the general case, without some mathematical analysis.

3. Repeat

Repeat the above step. The more you repeat, the smoother the result is. The images below were created by repeating the step 50 times. On my computer, generating a 128 by 128 tile takes about 10 minutes (Python implementation).

4. Convert Grayscale to RGB

Normalise the image, and map to a gradient.

texture1 texture2 texture7
texture4 texture6 texture3
texture8 texture9 texture10

Some example textures are shown above.



There are some things that I still want to investigate:

  • Is there a way to significantly speed up the algorithm?
  • Is there an intuitive way to linkthe matrix with the result?
  • What are the effects of starting with something other than white noise?


The code is currently too messy to release. I have built it on top of the code that was released for the Quadtrees article, so that is a good starting point if you do not want to wait. Otherwise, keep an eye out, I should post some code soon.


The quadtree is an important 2D data structure and forms the core of many spatial algorithms, including compression, collision detection, and stitching algorithms. Below you can download general purpose quadtree implementations in Java and Python.

The code accompanies the Quadtrees article in Dev.Mag. The tutorial explains how the implement a quadtree that can be use to store 2D data efficiently, lists what considerations there are in real-world applications, and gives some debugging tips.

Channels Compressed Simultaneously

bar bar_c
The original image (by smcgee). The image after being loaded into a quadtree.

Continue reading “Quadtrees”

Generating Random Numbers with Arbitrary Distributions

For many applications, detailed statistical models are overkill. Instead, we can get away with a rough description of the distribution – not in mathematical formula form, but just as a graph with a few sample points.

For example, when trying to model the traffic around a school, you might know that the graph looks something like this:


The input is the number of minutes before the first bell rings, and the output the number of children dropped off at that time. You know that most kids are brought before the bell rings, and that the closer to the bell, the more kids are being brought every minute. Only a few kids are late.

This tutorial describes how to generate random numbers that can generate a distribution described by an arbitrary (piece-wise linear) curve, as the one above.

Continue reading “Generating Random Numbers with Arbitrary Distributions”

Google App Engine for Games

Google App Engine has many properties that makes it suitable for indie development. Two articles in Dev.Mag look at GAE for game development (Issue 24 and Issue 25). The first is an overview of Google App Engine, with some focus on games. The second is a tutorial that explains the implementation of “Guess a Number” on Google App Engine, for which you can download the code. For the tutorial you will need:

Continue reading “Google App Engine for Games”