Genetic Algorithm based solver for jigsaw puzzles with piece size auto-detection.

Installation

Clone repo:

git clone https://github.com/nemanja-m/gaps.git
cd gaps

Install requirements:

poetry install

Install project locally:

pip install .

Creating puzzles from images

To create puzzle from image use gaps create

gaps create images/pillars.jpg puzzle.jpg --size=64

will create puzzle with 240 pieces from images/pillars.jpg where each piece is 64x64 pixels.

           

Run gaps create --help for detailed help.

NOTE: Created puzzle image dimensions may be smaller then original image depending on the given puzzle piece size. Maximum possible rectangle is cropped from original image.

Solving puzzles

In order to solve puzzles, use gaps run:

gaps run puzzle.jpg solution.jpg --generations=20 --population=600

This will start genetic algorithm with initial population of 600 and 20 generations.

Following options are provided:

Option	Description
--size	Puzzle piece size in pixels
--generations	Number of generations for genetic algorithm
--population	Number of individuals in population
--debug	Show the best solution after each generation

Run gaps run --help for detailed help.

Size detection

If you don't explicitly provide --size argument to gaps run, piece size will be detected automatically.

However, you can always provide gaps run with --size argument explicitly:

gaps run puzzle.jpg solution.jpg --generations=20 --population=600 --size=48

NOTE: Size detection feature works for the most images but there are some edge cases where size detection fails and detects incorrect piece size. In that case you can explicitly set piece size.

Termination condition

The termination condition of a Genetic Algorithm is important in determining when a GA run will end. It has been observed that initially, the GA progresses very fast with better solutions coming in every few iterations, but this tends to saturate in the later stages where the improvements are very small.

gaps will terminate:

• when there has been no improvement in the population for X iterations, or
• when it reaches an absolute number of generations

References

BibTeX entry:

@article{Sholomon2016,
  doi = {10.1007/s10710-015-9258-0},
  url = {https://doi.org/10.1007/s10710-015-9258-0},
  year = {2016},
  month = feb,
  publisher = {Springer Science and Business Media {LLC}},
  volume = {17},
  number = {3},
  pages = {291--313},
  author = {Dror Sholomon and Omid E. David and Nathan S. Netanyahu},
  title = {An automatic solver for very large jigsaw puzzles using genetic algorithms},
  journal = {Genetic Programming and Evolvable Machines}
}

License

This project as available as open source under the terms of the MIT License