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Magic Scissors ✂️

version downloads license python-version Colab

Generate synthetic data for computer vision using copy-paste context augmentation.

Magic Scissors is available as a Python package and a web application.

Installation

To install Magic Scissors, run the following command:

pip install magicscissors

Quickstart 🚀

To use Magic Scissors, you need two datasets:

  1. A dataset with object of interest;
  2. A dataset with backgrounds on which objects of interest can be pasted.

Both datasets should be formatted as COCO JSON. You can convert data between formats using Roboflow.

from magic_scissors import MagicScissors

data = MagicScissors(
    dataset_size=100,
    min_objects_per_image=1,
    max_objects_per_image=3,
    min_size_variance=2,
    max_size_variance=5,
    annotate_occlusion=0,
    working_dir="./",
    upload_to_roboflow=False,
    roboflow_api_key="",
    roboflow_workspace="",
    roboflow_project="",
)

# load data from COCO JSON files
data.load_backgrounds_from_coco()
data.load_objects_of_interest_from_coco()

# load data from Roboflow
data.download_objects_of_interest_from_roboflow(
    dataset_url=""
)
data.download_backgrounds_from_roboflow(
    dataset_url=""
)

# generate dataset and save to directory
data.generate_dataset()

Contributing 🏆

We would love your help improving Magic Scissors! Please see our contributing guide to get started. Thank you 🙏 to all our contributors!

License

This project is licensed under an MIT license.

Release files for magic-scissors 0.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for magic-scissors 0.1.0
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Built distribution (wheel)

Table of built distributions (wheels) for magic-scissors 0.1.0
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magic_scissors-0.1.0-py3-none-any.whl Python 3 none any Details

Total release size: 15.8 kB

Release files / magic-scissors-0.1.0.tar.gz

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