AI for CodeNames game ·

CLI

# Basic usage
python -m codenames.cli fox,dog
# Advanced usage
python -m codenames.cli fox,dog --opponent-agents cat,mouse --assassins duck --bystanders cow,goat --lang en
# Show help and exit
python -m codenames.cli --help

API

# Running in development mode
FLASK_APP=codenames.api:create_app FLASK_ENV=development flask run
# Running in production mode
waitress-serve --call codenames.api:create_app
# Example of POST /api/associations request
curl 'http://127.0.0.1:5000/api/associations' -H 'Content-Type: application/json' --data-binary \
     $'{"myAgents": ["fox"], "opponentAgents": ["cat"], "assassins": ["duck"], "bystanders": ["cow"], "lang": "en"}'
# Example of POST /api/detect request
curl 'http://127.0.0.1:5000/api/detect' -F 'image=@/path/to/the/image/photo.jpg'

Models

• word2vec in order to find similar words. Pre-trained russian version was taken from here.
• YoloV2 in order to locate cards in image. Model was trained using an implementation from ksanjeevan/dourflow. Trained model is available here. Data for training is available here.
• rotation model (based on MobileNetV2 architecture) in order to detect cards rotation. Trained model is available here. Data for training is available here.

Dev cheatsheet

# Linting
make lint
# Build docker image for production
make build-docker
# Run production image API on port 8080
make run-api-docker
# Downloads all trained models into the /models folder
python -m codenames.preloader
# Various preprocessing options for images
# split: cuts images into 3 pieces, in order to reduce number of cards in each image
# extract_cards: extracts all codenames cards into separate images
# rotate: fixes card image rotations
python -m codenames.preprocess extract_cards --in ./raw_images --out ./processed_images
# Train a rotation model. Input images should have a 0 degree rotation
python -m codenames.train_rotation --in ./cards --out ./model.h5
# Prints all words found on images
python -m codenames.detect_words --in ./cards