robotums / pytspart

Easy to use and fast TSP art generation with weighted Voronoi Stippling.

Features of This Repo:

1. Travelling Salesperson Art

gets converted to:

2. Weighted Voronoi Stippling

This is a replication of the following article:

Weighted Voronoi Stippling, Adrian Secord. In: Proceedings of the 2nd International Symposium on Non-photorealistic Animation and Rendering. NPAR ’02. ACM, 2002, pp. 37– 43.

where the author introduced a techniques for generating stipple drawings from grayscale images using weighted centroidal Voronoi diagrams as in the traditional artistic technique of stippling that places small dots of ink onto paper such that their density give the impression of tone.

Setup:

This replication has been written and tested on OSX 10.12 (Sierra) using the following packages:

• Python 3.6.0
• Numpy 1.12.0
• Scipy 0.18.1
• Matplotlib 2.0.0
• tqdm 4.11.2
• Pillow 4.0.0
• Imageio 2.9.0

Setup Instructions (for TSP):

0. Make a venv for this repo (recommended). Python 3.5+ works best: python3 -m venv TSP

1. activate the venv and use pip to install the following: pip3 install numpy pandas matplotlib Pillow cython

2. You'll need the concorde TSP solver -- one of the most powerful TSP solvers around, and to get in it python, you'll have to do the following: a. clone the python wrapper library

git clone https://github.com/jvkersch/pyconcorde
cd pyconcorde

b. run pip install (dont forget the period). This may take a minute.

pip3 install -e .

c. verify installation. open up the python interpreter and type: from concorde.tsp import TSPSolver And make sure it doesn't fail.

Usage

usage: generate_tsp.py [-h] filepath

positional arguments:
  filepath    location of source image

optional arguments:
  -h, --help  show this help message and exit

usage: tsp.py [-h] [--time_bound n] [--verbosity bool] [--norm str] [--save]
              [--display] [--output_filename OUTPUT_FILENAME]
              .dat/.npy filename

TSP art from stippled image generator

positional arguments:
  .dat/.npy filename    location of .dat/.npy filename, output of stipple.py

optional arguments:
  -h, --help            show this help message and exit
  --time_bound n        Upperbound for TSP solver to find solution.
  --verbosity bool      Verbosity of TSP solver.
  --norm str            edge weight for TSP solver
  --save                Save computed points
  --display             Display final result
  --output_filename OUTPUT_FILENAME
                        Solved filename

 usage: stippler.py [--n_iter n] [--n_point n] [--save] [--force]
                    [--pointsize min,max] [--figsize w,h]
                    [--display] [--interactive] file

 Weighted Vororonoi Stippler

 positional arguments:
   file                  Density image filename

 optional arguments:
   -h, --help            show this help message and exit
   --n_iter n            Maximum number of iterations
   --n_point n           Number of points
   --pointsize (min,max) (min,max)
                         Point mix/max size for final display
   --figsize w,h         Figure size
   --force               Force recomputation
   --save                Save computed points
   --display             Display final result
   --interactive         Display intermediate results (slower)

For more info:

Check out Opt Art: From Mathematical Optimization to Visual Design by Robert Bosch.