Waffle Utils 🥛
Project description
Waffle Utils
- waffle util tools
- Waffle Data Convention
Install
- python >= 3.9
pip install waffle_utils
Examples
Create Dataset from coco format
both example below will result same output
Python Code
from waffle_utils.dataset import Dataset
from waffle_utils.dataset.format import Format
url = "https://github.com/snuailab/waffle_utils/raw/main/mnist.zip"
dummy_zip_file = "mnist.zip"
dummy_dataset_name = "mnist"
dummy_extract_dir = "tmp/extract"
dummy_coco_root_dir = "tmp/extract/raw"
dummy_coco_file = "tmp/extract/exports/coco.json"
network.get_file_from_url(url, dummy_zip_file, create_directory=True)
io.unzip(dummy_zip_file, dummy_extract_dir, create_directory=True)
ds = Dataset.from_coco(
dummy_dataset_name,
dummy_coco_file,
dummy_coco_root_dir,
)
ds = Dataset.from_directory(dummy_dataset_name, dummy_data_root_dir)
ds.split_train_val(train_split_ratio=0.8)
ds.export(Format.YOLO_DETECTION)
CLI
wu get_file_from_url --url https://github.com/snuailab/waffle_utils/raw/main/mnist.zip --file-path tmp/mnist.zip
wu unzip --url tmp/mnist.zip --output-dir tmp/extract
wu from_coco --name mnist --coco-file tmp/extract/exports/coco.json --coco-root-dir tmp/extract/raw
wu split_train_val --name mnist --train-split-ratio 0.8
wu export --name mnist --export-format yolo_detection
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