Procedural Content Generation with Clojure

1
Procedural Content
Generation with Clojure
Mike Anderson

2
Contents
1. Procedural Content Generation
2. Introducing Clojure
3. Creating content with Clisk

3
Definition
Procedural content generation (PCG)
is the programmatic generation
of content using algorithms, which may
incorporate random or pseudo-random
processes

4
Some applications
Content Types Examples
Images
• 3D model textures
• Abstract art
Pioneering work by Ken
Perlin (inventor of Perlin
noise)
Music
• Melodies
• Synthesised sounds
Work by Jeremy
Leach
http://algorithmiccomposition.org/
Game content
• Game items
• Random maps
Roguelike games
Virtual Worlds
• Landscapes
• Plants / animals
• Alien races
No Man’s Sky
(planned release 2016)

5
Image Generation
(0,0)
y-axis
(0,1) (1,1)
(1,0)
pixel colour = f(x, y)
x-axis

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Image “Composition”
x
f(x, y) g(x, y)
=
f(x, y) * g(x, y)
The Big Idea: Use a functional language to compose images!

7
Contents

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What is Clojure?
JVM Language
Functional Dynamic

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Data types – The Usual
Type Representation
Long integers 42
Double precision reals 3.14159265359
Strings “Hello World”
Characters M
Hex 0xFF8000
All represented by equivalent Java
Objects “under the hood”

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Data types – The Not-So Usual
Type Representation
Keywords :foo
Symbols 'hello
Ratios 1/3
Regexes #"[0-9]+"
BigInt 15511210043330985984000000N
BigDecimal 189675.1678969698698969986M
Base-N numbers 3r1201200

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Data types – Collections
Type Representation
Maps {:foo 10
:bar 20}
Sets #{2 3 5 7 11 13}
Vectors [1 2 3 :foo]
Lists '(foo a b c)
All collections in Clojure are Immutable!

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Lisp = “Lots of Irritating Silly Parentheses”?
myfunction(alpha, beta)C-like languages
(myfunction alpha beta)Lisp
Usually the same number of parentheses!

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Expressions
Function
application
=> (+ 11 31)
42
Nested
Expressions
=> (+ 11 (* 30 30))
911
Local definitions
=> (let [x 11]
(* x 100))
1100

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Functions
Function definition
=> (defn triple [x]
(* x 3))
=> (triple 10)
30
Higher order
functions
=> (map triple [1 2 3])
(3 6 9)

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“Code is Data”
=> (+ 2 3)
5
=> '(+ 2 3)
(+ 2 3)
=> (eval '(+ 2 3))
5

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Java Interop
Java myObject.doSomething(foo, bar);
Clojure (.doSomething myObject foo bar)

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Contents

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https://github.com/mikera/clisk
To follow along:
- Clone the repo at: https://github.com/mikera/clisk
- All examples are in: src/test/clojure/clisk/workshop/

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Collaborative spirit – all about sharing!
Open Source Project
Open Source Language
Open Source content “Recipes”

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Example from “TweeGeeMee”
https://twitter.com/tweegeemee/status/676985464813953029

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@TweeGeeMee twitter bot by Roger Allen
@TweeGeeMee evolved images

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Example from “Clevolution”
https://twitter.com/deathbob/status/661294702759669760

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Image Generation
(0,0)
y-axis
(0,1) (1,1)
(1,0)
pixel colour = [x 0 0]
x-axis
REPL
Command
(show [x 0 0])

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Image Generation
(0,0)
y-axis
(0,1) (1,1)
(1,0)
pixel colour = [x y 0]
x-axis
REPL
Command
(show [x y 0])

25
Implementation notes
• Clisk functions actually generate ASTs
• “show” function compiles the AST into efficient low-
level code, something like*:
• Lisp makes this (comparatively) easy!
This is very simplified! The real compiled code includes support for multi-threading,
caching of texture objects, making use of fast primitive maths etc.
*
(dotimes [y height]
(dotimes [x width]
(let [colour ....calculation code....]
(.setRGB image x y colour))))

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Many primitives
Solid colours (show [0.1 0.7 1.0])
Patterns (show (checker red pink))
Texture maps (show clojure)

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Transformations - scaling
Base (show (checker red pink))
Scaled
(show
(scale 0.3
(checker red pink)))

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Transformations – offsets
Offset
(show
(offset
[0.2 0.2]

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Transformations – warps
Warped
(show
(warp
(scale 0.2 vnoise)

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Noise functions
Monochomatic
noise
(show (scale 0.2 noise))
Vector noise (show (scale 0.2 vnoise))
Plasma (show (scale 0.2 plasma))

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Images from the Gallery: Stained Glass
(show (let [voronoi1 (voronoi :points 512)]
(v*
(v* 20.0 (voronoi-blocks :voronoi voronoi1))
(warp (voronoi-points :voronoi voronoi1) grain)))
:size 512)
Key techniques:
• Use a Voronoi map to create “cells”
• Multiply with the psuedo-random “grain” function to colour cells

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Images from the Gallery: Mandelbrot
(show (viewport [-2 -1.5] [1 1.5]
(fractal
:while (v- 2 (length [x y]))
:update (v+ c [(v- (v* x x) (v* y y))
(v* 2 x y)])
:result (vplasma (v* 0.1 'i))
:bailout-result black
:max-iterations 1000)) :size 256)
Key techniques:
• Use the “fractal” function to create an iterative process
• Simulate complex numbers (x+ iy) using regular clisk vectors
• Use a viewport to see the relevant region of the Mandelbrot set

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Images from the Gallery: Voronoi Fractal
(show (let [voronoi1 (voronoi :points 32)]
(fractal
:while (v- 0.97 (voronoi-function
(v- 1 (v* (vdivide (v- y x) y)
(vdivide (v- z x) z)))
:voronoi voronoi1))
:update (v+ (v* pos 2) pos)
:result (vdivide 3 (v+ 3 'i))
:max-iterations 4)) :size 512)
Key techniques:
• Voronoi map used to create a “net” pattern
• Use the “fractal” function to create iterative layers
• If it hits the net, stop
• Else continue to next layer with a scaled version of the net

Procedural Content Generation with Clojure

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