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https://github.com/fabridigua/LogicGamesSolver

A Python tool to solve logic games with AI, Deep Learning and Computer Vision
https://github.com/fabridigua/LogicGamesSolver

artificial-intelligence computer-vision constraint-satisfaction-problem deep-learning logic-games sudoku

Last synced: 29 days ago
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A Python tool to solve logic games with AI, Deep Learning and Computer Vision

Host: GitHub
URL: https://github.com/fabridigua/LogicGamesSolver
Owner: fabridigua
License: gpl-3.0
Created: 2021-01-18T20:03:02.000Z (almost 4 years ago)
Default Branch: main
Last Pushed: 2021-01-30T19:17:05.000Z (almost 4 years ago)
Last Synced: 2024-08-04T01:16:48.368Z (4 months ago)
Topics: artificial-intelligence, computer-vision, constraint-satisfaction-problem, deep-learning, logic-games, sudoku
Language: Python
Homepage:
Size: 8.34 MB
Stars: 15
Watchers: 1
Forks: 3
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

ai-game-devtools - LogicGamesSolver

README

# LogicGamesSolver
Python tool for solving logic games with AI, Computer Vision and a pinch of Deep Learning.

----------------

Scre

------

## Table of contents

* [Basic Overview](#basic-overview)
* [Project structure](#project-structure)
* [System Requirements](#system-requirements)
* [Setup](#setup)
* [How it works](#how-it-works)
* [Games included](#games-included)
* [References](#references)

## Basic Overview

This project combines Computer Vision and Artificial Intelligence to solve logic puzzle games like *Sudoku*, *Stars Battle* and *Skyscrapers*.

The execution consists of 2 phases:

| 1. Board Detection | 2. Game solving |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| | |
| The software detects the board showed by the user (in real-time or analyzing a local image). Then analyzes the structure to understand the needed informations to solve the game. | The information collected is then used to solve the puzzle considering it as a Constraints Satisfaction Problem using a Backtracking algorithm to find the solution, given the game rules. |

**Language**: *Python*

**Frameworks**: *OpenCV*, *Tensorflow*

**Algorithms**: Constraints Satisfaction Problem Backtracking, Digits Classifier (CNN), Image Contours Finding and Warping

## Project structure

```
LogicGamesSolver
│ README.md
│ LICENSE
│
└───imgs
│ │ screen_XXX Screens for project explaination
│ │ ...
│
│ main.py Main file to execute the software
│ DigitClassifier.py Class for digit classification with a pretrained CNN
│ PuzzleDetector.py Class for puzzle detection and analyze from an image
│ Solver.py Class for solving the games given puzzle's informations and rules
```

## System Requirements

- **Python 3.8**

- **Numpy 1.19.2**

- **OpenCV 4.0.1**

- **Tensorflow 2.3.0**

## Setup

To run the project, clone it with [Git] and run the `main.py` file with desired parameters:

[Git]: https://git-scm.com/downloads "Git download page"

```
$ git clone https://github.com/fabridigua/LogicGamesSolver
$ cd LogicGamesSolver
$ python main.py sudoku 9 3
```

There are 4 arguments you can set:

[**game** name of the game you want to solve] = sudoku | skyscrapers | stars [default: sudoku]

[**grid_len** number of squares in the side of the puzzle's grid] = *Integer* es. 5 [default: 9]

[**square_len** for sudoku, number of squares in an area] = *Integer* es. 2 [default: 3]

[**is_realtime** for coose between local or real-time execution] = *Boolean* es. true [default true]

Note that if you set **is_realtime** to *false* you have to put an image file named *input_[**game**]*.*png* es. `input_stars.png`

## How it works

This project touches many fields of study:

1. **Computer Vision** for board detection
2. **Deep Learning** for puzzle analyzing
3. **Artificial Intelligence** for puzzle solving

##### 1. Board Detection

The software analyzes the image in input looking for the biggest *contour*^{[[1]](#cont_ref)} in the scene.

Once found the puzzle, its image is warped applying a perspective transformation to make the puzzle image a plane.

In *real-time* mode, the user must press `space` key to go ahead when the software is recognizing well the puzzle.

##### 2. Puzzle Analyzing

The software analyzes the puzzle board to retrieve the needed informations to solve the game.

If the puzzle includes numbers (like *Sudoku* and *Skyscrapers*) a **Convolutional Neural Network** for digit classifying (trained with the fabulous *MNIST*^{[[2]](#mnist_ref)} dataset) is executed.

For the *Stars* game, the board image is processed to find the inner areas. Using *OpenCV* methods, the boldest contours are highlighted and then the *connected components*^{[[3]](#connected_ref)} are analyzed (and drawn in different colors) to find the areas of the grid.

##### 3. Game solving

Once collected all the informations about the structure of the puzzle, the game is represented as a CSP (Constraint Satisfaction Problem) and solved applying a backtracking algorithm. By the game point of view, the CSP^{[[4]](#csp_ref)} consists in 3 elements:

- *Variables*: the grid cells to fill

- *Domains*: the sets of values that each cell can assume

- *Constraints*: the game's rules

If the input is correct, the algorithm finds the solution with 100% of accuracy, but it can takes a long time basing on grid length (and so the number of variables) and size of domains. For a normal *Sudoku* scheme it takes 3-5 seconds.

For more details see the article.

## Games Included

For now, this projects includes the detection and solving procedures of these games:

- **Sudoku** [ ex. `python main.py sudoku 9 3` ]

*Description*: given a grid with some number in range [1,9] , fill the empty cells respecting the rules.

*Parameters*:

**grid_len**: number of squares in each row, column and inner area (usually 9)

**square_len**: number of square in each row and column of a inner area (usually 3)

*Rules*:

- Every row, column and area have to contain all number between 1 and 9

- **Stars** [ex. `python main.py stars 8 1` ]

*Description*: given a grid divided into regions insert a star in each row, column and sector.

*Parameters*:

**grid_len**: number of squares in each row and column

**star_number**: number of stars in a inner area (usually 1)

*Rules*:

- Every row, column and sector have to contain only a star (or more if setted with *star_number* argument)
- Stars can't be in adjacent cells

- **Skyscrapers** [ ex. `python main.py skyscrapers 8` ]

*Description*: given a grid with some numbers near the sides, fill the grid with the number from 1 to **grid_len**.

*Parameters*:

**grid_len**: number of squares in each row and column

*Rules*:

- Every row, column and have to contain all number between 1 and **grid_len**
- The numbers along the edge of the puzzle indicate the number of buildings which you would see from that direction if there was a series of skyscrapers with heights equal the entries in that row or column.

## References

[1]: [Contours : Getting Started]

[Contours : Getting Started]: https://docs.opencv.org/3.4/d4/d73/tutorial_py_contours_begin.html "Contours : Getting Started"

[2]: [MNIST Dataset]

[MNIST Dataset]: http://yann.lecun.com/exdb/mnist/ "MNIST Dataset"

[3]: [The connected-component labeling problem: A review of state-of-the-art algorithms]

[The connected-component labeling problem: A review of state-of-the-art algorithms]: https://www.sciencedirect.com/science/article/pii/S0031320317301693 "The connected-component labeling problem: A review of state-of-the-art algorithms"

[4]: [Constraint satisfaction problem]

[Constraint satisfaction problem]: https://en.wikipedia.org/wiki/Constraint_satisfaction_problem "Constraint satisfaction problem"