Genetic Algorithm based solver for large jigsaw puzzles with piece size auto-detection.
Based on the "Computer Vision" category.
Alternatively, view gaps alternatives based on common mentions on social networks and blogs.
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
Do you think we are missing an alternative of gaps or a related project?
Genetic Algorithm based solver for jigsaw puzzles with piece size auto-detection.
Clone repo:
$ git clone https://github.com/nemanja-m/gaps.git
$ cd gaps
Install requirements:
$ pip install -r requirements.txt
$ sudo apt-get install python-tk
Install project in editable mode:
$ pip install -e .
To create puzzle from image use create_puzzle script.
i.e.
$ create_puzzle images/pillars.jpg --size=48 --destination=puzzle.jpg
[SUCCESS] Puzzle created with 420 pieces
will create puzzle with 420 pieces from images/pillars.jpg where each piece is 48x48 pixels.
Run create_puzzle --help for detailed help.
NOTE Created puzzle dimensions may be smaller then original image depending on given puzzle piece size. Maximum possible rectangle is cropped from original image.
In order to solve puzzles, use gaps script.
i.e.
$ gaps --image=puzzle.jpg --generations=20 --population=600
This will start genetic algorithm with initial population of 600 and 20 generations.
Following options are provided:
| Option | Description |
|---|---|
--image |
Path to puzzle |
--size |
Puzzle piece size in pixels |
--generations |
Number of generations for genetic algorithm |
--population |
Number of individuals in population |
--verbose |
Show best solution after each generation |
--save |
Save puzzle solution as image |
Run gaps --help for detailed help.
If you don't explicitly provide --size argument to gaps, piece size will be detected automatically.
However, you can always provide gaps with --size argument explicitly:
$ gaps --image=puzzle.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.
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:
X iterations, orBibTeX 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}
}
This project as available as open source under the terms of the MIT License
*Note that all licence references and agreements mentioned in the gaps README section above
are relevant to that project's source code only.
Do not miss the trending, packages, news and articles with our weekly report.