To run a few examples (which will be saved as example*.png in this directory), run
julia interpretertest.jl
The results, in order, are a simple example of displacement based on fuzzy selection, the berry example from the report, and a fractal tree.
This runs the interpreter in interpreter.jl on a couple of the tests defined in examples.py.
This processes the input AST in the script frontend.py to produce the intermediate representation, which is then interpreted by interpreter.jl.
This assumes you have Julia installed, with the packages DataStructures, Plots, Images, and PyCall installed. To install a package, within the Julia command prompt execute:
using Pkg
Pkg.add(“PackageName”)
Note that runtimes sometimes appear to be long, but the vast majority of the time is spent loading packages. Julia is not meant to be reloaded for individual small tasks. However, the backend algorithms themselves are orders of magnitude faster than their python counterparts.
The frontend python code used to generate the image is in examples.py (each getCmds() function is a separate example) and the code is compiled and run using interpretertest.jl in Julia.
A frontend that optimizes the intermediate representation is found in frontend.py, and an example programs are located in examples.py.
The primitive operations on shapes are defined in CSG.jl, while the code pertaining to discretization is located in canvas.jl, and the algorithms for computing SDF values are located in fast_marching.jl. selectors.jl contains definitions of field objects, along with code for discretizing and sampling fields. Some additional math functions are defined in mathutil.jl.