mtgdata 0.3.0

MTG image dataset with automatic image scraping and conversion.

🧙‍♂️ Magic The Gathering ✨
🧚‍♀️ Dataset 🧝‍♀️

With automated multithreaded image downloading,
caching and optional dataset conversion.

Example reconstructions of dataset elements
using a simple Beta-VAE

⚡️  Quickstart

1. Install mtgdata with pip install mtgdata

2. Prepare or convert the MTG data using the command line with python -m mtgdata --help

📋  Features

MTG Card Face Dataset

• Automatically scrape and download card images from Scryfall
• Multithreaded download through a randomized proxy list
• Only return valid images that are not placeholders
• Return all the faces of a card
• Normalise data, some images are incorrectly sized
• Cached

Convert to HDF5

• Convert the data to an hdf5 dataset
• Much faster than raw jpg or png image accesses
• Metadata json file allows linking back to original scryfall information.

Pickle HD5F Dataset Class

• Load the converted HDF5 dataset from disk from multiple threads / processes

⬇️  Download Images

Command Line

You can prepare (download) all the normal quality images from the default Scryfall bulk data by running mtgdata/__main__.py:

python3 mtgdata prepare --help

Otherwise, you can instead convert the downloaded images into an hdf5 dataset by running:

python3 mtgdata convert --help

Programmatically

Alternatively you can download the images from within python by simply instantiating the mtgdata.ScryfallDataset object. Similar arguments can be specified as that of the command line approach.

from mtgdata import ScryfallDataset, ScryfallImageType, ScryfallBulkType

data = ScryfallDataset(
    img_type=ScryfallImageType.small,
    bulk_type=ScryfallBulkType.default_cards,
    transform=None,
)

# you can access the dataset elements like usual

Proxy Issues?

The scrape logic used to obtain the proxy list for mtgdata.utils.proxy.ProxyDownloader will probably go out of date. You can override the default scrape logic used by the Dataset download logic by registering a new scrape function.

from doorway.x import proxies_register_scraper
from typing import List, Dict

@proxies_register_scraper(name='my_proxy_source', is_default=True)
def custom_proxy_scraper(proxy_type: str) -> List[Dict[str, str]]:
    # you should respect this setting
    assert proxy_type in ('all', 'http', 'https')
    # proxies is a list of dictionaries, where each dictionary only has one entry:
    # - the key is the protocol
    # - the value is the matching full url
    return [
        {'HTTP': 'http://<my-http-proxy>.com'},
        {'HTTPS': 'https://<my-https-proxy>.com'},
    ]

🔄  Convert Images to an HDF5 Dataset

Command Line

The images can be convert to hdf5 format by running the file mtgdata.scryfall_convert. Various arguments can be specified, please see the argparse arguments at the bottom of the file for more information.

python3 mtgdata/scryfall_convert.py

The resulting data file will have the data key corresponding to the images data.

Programmatically

Alternatively you can convert and generate the hdf5 dataset from within python by simply calling the mtgdata.scryfall_convert.generate_converted_dataset function. Similar arguments can be specified as that of the command line approach.

from mtgdata import generate_converted_dataset, ScryfallImageType, ScryfallBulkType

generate_converted_dataset(
    out_img_type=ScryfallImageType.small,
    out_bulk_type=ScryfallBulkType.default_cards,
    save_root='./data/converted/',
    out_obs_size_wh=(224, 160),
    convert_speed_test=True,
)

Loading The Data

We provide a helper dataset class for loading this generated file.

from torch.utils.data import DataLoader
from mtgdata import Hdf5Dataset


# this h5py dataset supports pickling, and can be wrapped with a pytorch dataset.
data = Hdf5Dataset(
    h5_path='data/converted/mtg-default_cards-normal-60459x224x160x3.h5',  # name will differ
    h5_dataset_name='data',
    transform=None,
)

# you can wrap the dataset with a pytorch dataloader like usual, and specify more than one worker
dataloader = DataLoader(data, shuffle=True, num_workers=2, batch_size=64)

# to load the data into memory as a numpy array, you can call `data = data.numpy()`
# this will take a long time depending on your disk speed and use a lot of memory.