visudoku

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Solves a sudoku puzzle by looking at it

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# ViSudoku

This is a visual sudoku solver. It takes a picture of a sudoku puzzle as input and then solves and prints the solution.

The whole opencv workflow is explained in `visudoku.ipynb`.

Training data (570 images) was generated by hand. Then, it was augmented in the following ways:
 - added black border of {1..4}px around the image to simulate smaller digits
 - removed black border of {1..4}px from the image to simulate larger digits
 - rotated {4, 8, 12, 16} degrees clockwise and anticlockwise to simulate weird orientations

Digit recognition is done using K-Nearest Neighbours (knn).

The final puzzle is solved by a cpp program (here as a submodule) which I wrote a few years ago.

## UPDATE!
Now I have 1332 images, and a better model. The augmentation was limited to {1..2}px scaling and {4, 8} degrees rotation.

It performs wayy better now!

## Screenshots
<img src="./screens/Original.png" alt="Original" height="400px" />
<img src="./screens/Preprocessed.png" alt="After Preprocessing" height="400px" />
<img src="./screens/GettingTheBoundary.png" alt="Getting boundary of the puzzle" height="400px" />
<img src="./screens/ExtractingThePuzzle.png" alt="Extracting the puzzle" height="400px" />
<img src="./screens/GettingTheDigits.png" alt="Getting the digits" height="400px" />
<img src="./screens/DigitRecognition.png" alt="Digit recognition" height="400px" />

### An example

## Instructions

### Docker
Make sure you have [docker](https://www.docker.com/) installed!

You can either build the image yourself (takes a lot of time) or use the online one

#### Build locally

To get started, run
```sh
./setup.sh
docker run -p <port>:8080 -d --name visudoku visudoku 
```

#### Download built image
Simply run
```sh
docker run -p <port>:8080 -d --name visudoku yoogottamk/visudoku
```

You can check the app at the port you specified

To stop/start the container, run
```sh
docker stop/start visudoku
```

### Normal

Before anything else, please [create a python3 virtual environment](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/#creating-a-virtual-environment) and install the stuff in [requirements.txt](./requirements.txt)

```sh
python3 -m venv venv
. venv/bin/activate

pip install -r requirements.txt
```

#### Please first follow the instructions given in [digit-recog](./digit-recog)

### Flask
```sh
./flask-setup.sh
cd flask-app
python server.py
```

You can check the app at [port 8080](http://localhost:8080)

### Jupyter

*NOTE: You first need to install jupyter notebook/lab first*

Run `jupyter notebook` and open `visudoku.ipynb`.

You can either use the images given in `test-images`, or provide your own images, or even use the webcam.
To use the webcam, you have to provide an invalid image pat.

If you use the webcam, you have to press `c` when you want to capture the image.

After getting the image, it will automatically solve and give the solution at the bottom.

## Aside
This project began a year ago, I could only work on it in my vacations.

#### winter'18
 - Decided that I wanted to make something like this
 - Finished the opencv part (everything till digit extraction)
 - Got stuck at digit recognition.

#### summer'19
 - Took [a course on ml](https://www.coursera.org/learn/machine-learning)
 - Tried to make it recognize the digits, failed terribly
 - Realised that digit extraction is really bad, rewrote it
 - Wrote a [solarized-dark theme usercss](https://github.com/YoogottamK/jupyter-notebook-solarized-dark) for jupyter notebook and jupyterlab

#### winter'19
 - Tried digit recognition again, using a CNN, accuracy: 9%
 - Tried digit recognition using knn, accuracy: 99.69%
 - Put up all the pieces together

##### TODO: organize this in a better manner, create a better frontend, and tidy up the code

visudoku

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