Convert images into low-polygon-count versions.
It works by optimizing a set of points, which are then triangulated using Delaunay to produce a set of polygons. The color of each polygon is determined by the original image's color at the polygon barycenter.
The optimizer downscales the image by a customizable factor, but the final result is of the same size (by default) as the original image, with super sampling anti aliasing to reduce artifacts.
Clone the repository, navigate to the folder and install the requirements using poetry or pip
Using pip
pip install -r requirements.txt
Using poetry
poetry install
Simply run python main.py or poetry run python main.py
The configuration is in main.py:
| Constant | Values | Description |
|---|---|---|
| OPTIMIZER | Optimizer.[GA | SA | PSO | CMA] | The optimizer to use: Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization, Covariance Matrix Adaptation. |
| OPTIMIZATION_PROBLEM | Problem.[DELAUNAY | HILLCLIMB] | The optimization problem to optimize. Hillclimb is only for debug / learning purposes. |
| IMAGE_PATH | <str> | Path to the image to convert. |
| IMAGE_DOWNSCALING | <int> | Downscaling factor for the optimization process (does not impact the final result). |
| POP_SIZE | <int> | Number of individuals. |
| MAX_STEPS | <int> | Number of optimization steps until termination. |
| N_POINTS | <int> | Number of points to triangulate. |
| EXPLORATION_FACTOR | <float> | SA Strength of mutation. |
| TAU_INI | <float> | SA & GA Temperature / selectivity pressure (initial) |
| TAU_END | <float> | SA & GA Temperature / selectivity pressure (final) |
| MUTATION_RATE | <float> | GA Probability of mutating each gene in the genome. |
| MUTATION_STRENGTH | <float> | GA Multiplicative coefficient for the mutation. |
| ELITISM | <int> | GA Number of best individuals to keep at each generation. |
| CROSSOVER_POINTS | <int> | GA Number of genome-sub-sequence to choose from each parent during crossover. |
| INERTIA | <float> | PSO Inertia of velocity. |
| PHI_COGNITIVE | <float> | PSO Velocity weight for the particle's best found position. |
| PHI_SOCIAL | <float> | PSO Velocity weight for the swarm's best found position. |
| VELOCITY_STRENGTH | <float> | PSO Initial velocity factor. |
| SIGMA | <float> | CMA Initial step size |
The provides interfaces to customize both the optimizer or the optimization problem.
For example, it is possible to define a new optimization problem simply by implementing the Individual abstract class, and then use an optimizer to minimize its loss.
This project has been done has work for the CMDO course @UNIBO-AI 2020/21, so performance was not its main objective. For this version no CLI or library is planned, but in the future a faster version made in Rust will be released.