Monopoly recognition

Technique and approach

The project is based on classic Computer Vision techniques, hence, no Neural Networks are involved.
The notebook recognize (executable code) is self-explanatory, so the detailed description of how everything works is present there.

Set-up

1. After cloning the repository, create a Python 3.8 virtual environment:

python3.8 -m venv venv

2. Activate it:

source vevn/bin/activate 

3. Install the required libraries:

pip install -r requirements.txt

Data-set

There are 3 levels of difficulty:

Easy

1. The view is nearly vertical.
2. The light is very good.
3. There are almost no shadows.
4. The board is straightly positioned.
5. The pawns are never covered.
6. Less moves.

Medium

1. The view is nearly vertical.
2. Quite bad light.
3. Soft shadows are presents.
4. The board may be tilted.
5. The pawn may be partially covered by fingers.
6. More moves.

Hard

1. Angled view.
2. Awful light.
3. Extreme shadows.
4. The board may be tilted.
5. The pawn may be heavily covered by fingers.
6. More moves.

File-structure

1. data/

   a) input videos separated by difficulty level

   b) train/ contains heuristic templates

2. output/

   a) rendered videos siparated by difficulty level

   b) intemediate/ contains output demos obtained while processing

3. Project files

   a) recognize.ipynb is the notebook with how it works explanations and code

   b) requirements.txt is the list of dependencies